Apply uniform min128 dataset scope and compact Overall rows

Use the user-authorized 137-dataset scope (mean valid sequence length >=128), retain source archives, and gate FreshRetailNet-LT and Kaggle Web Traffic pending corrected full inputs. Remove redundant Overall row subtitles.

FINAL160_OPERATOR_SCOPE_AUDIT.json

@@ -0,0 +1,93 @@
+{
+  "current_input_semantics": {
+    "FreshRetailNet-50K_corrected_column_count": 114,
+    "FreshRetailNet-50K_old_feed_column_count": 17,
+    "FreshRetailNet-LT_corrected_target_column_count": 114,
+    "Kaggle_Web_Traffic_corrected_target_column_count": 1,
+    "audited_unchanged_rows_may_be_retained": {
+      "traqq_weekly_activity": 5,
+      "us_stock_financial_ratios": 39
+    },
+    "changed_and_require_rerun": {
+      "freshretailnet_50k": 114,
+      "freshretailnet_lt": 114,
+      "github_top_projects": 3,
+      "kaggle_web_traffic": 1,
+      "magseven_ml_ready_high_frequency_anomaly": 6,
+      "trending_stocks_yahoo_finance": 8
+    },
+    "pending_input_acceptance_datasets": [
+      "freshretailnet_lt",
+      "kaggle_web_traffic"
+    ]
+  },
+  "extra_historical_ids": [
+    "jovian_lightning",
+    "macroecon",
+    "nasa_maven_kp_insitu",
+    "weather_data_NC"
+  ],
+  "feed_dataset_count": 160,
+  "feed_rows": 41090,
+  "final160_dataset_count": 160,
+  "final160_intersection_count": 156,
+  "inputs_sha256": {
+    "corrected_input_requirements": "391a5f081f0347925c5c1187fae2b4fe85c3ae88ec44f04a0fb0881efa42b7c7",
+    "descriptive_inventory": "8268a73a536c70a2d8d01a8fca33d2a7c07e19d9f888ea92ef488c1801702c94",
+    "final160_scope": "92a995e4278971077fe60dabd913e26e1afdccaeca1ec6181ae1de3f50929889",
+    "format_audit": "2d3fb152f4f040a044a49fb23a0a34eb217ad0a54668f1fa0c65281d72064ba8",
+    "operator_feed": "c6962b9e57a59b50a2e4fa0dc0db9789541a763ea46f4b0cfb7cafd3b88dd22d"
+  },
+  "minimum_replacement_campaign": {
+    "breakdown": {
+      "FreshRetailNet-50K_114_ready_results_pending": 1140,
+      "already_accepted_corrected_three": 170,
+      "missing_four": 580,
+      "pending_input_acceptance_FreshRetailNet-LT_and_Kaggle": 1150
+    },
+    "column_keys": 304,
+    "dispatch_gate": "FreshRetailNet-LT and Kaggle exact typed inputs must first receive independent FROZEN_READY acceptance",
+    "do_not_reuse_stale_changed_rows": true,
+    "exact_key_plan": "analysis_outputs/paper_final160_refresh_20261013_v2/OPERATOR_FINAL160_RERUN_KEYS.csv",
+    "exact_key_plan_sha256": "f78efdd3be2df37bef865de705bafd2f78d32df6e9e062d731eb916ab7693405",
+    "recipe_keys": 3040,
+    "retain_existing_current_rows": true
+  },
+  "missing_final160_column_counts": {
+    "METR-LA": 34,
+    "PEMS-BAY": 10,
+    "m2oe2_timeseries": 13,
+    "medium_posts": 1
+  },
+  "missing_final160_ids": [
+    "METR-LA",
+    "PEMS-BAY",
+    "m2oe2_timeseries",
+    "medium_posts"
+  ],
+  "paper_conclusion": "The 41,090-row feed is historical evidence, not a valid final160/current-input Fig. 11 source; the exact repair is now 3,040 recipe keys, of which 1,150 remain blocked on two input acceptances.",
+  "projected_exact_final160_union_rows": 35870,
+  "recipe_count": 10,
+  "recipe_ids": [
+    "corad_tricklet_dictionary_pair_v1",
+    "fpzip_range_pair_v1",
+    "gorilla_trailing_pack_pair_v1",
+    "project_arithmetic_bytes_v1",
+    "project_huffman_bytes_v1",
+    "project_rans_reference_v1",
+    "rle_run_length_direct_v1",
+    "snappy_token_format_pair_v1",
+    "spdp_entropy_pair_v1",
+    "stream_vbyte_integer_code_pair_v1"
+  ],
+  "runner_contract": [
+    "Prepare canonical per-column manifests from the frozen final160/current-input sources before dispatch.",
+    "Do not dispatch FreshRetailNet-LT or Kaggle keys from target-schema rows; wait for exact independently accepted typed inputs and bind their real manifests.",
+    "Run the same ten diagnostic recipes with fresh encode/decode processes and round-trip/byte checks.",
+    "The byte-effect path carries null timing fields; do not compare or pool timing across hosts.",
+    "Preserve the historical 41,090-row feed and write a new attempt/union.",
+    "The optimized WiDS runner may be reused only after binding exact recipe CLI/source/runtime/input SHAs and preserving its process/amortization contract."
+  ],
+  "schema": "paper-final160-operator-feed-audit-v2",
+  "status": "BLOCKED_REQUIRES_TWO_INPUT_ACCEPTANCES_AND_EXACT_SCOPE_CURRENT_INPUT_RERUN"
+}

README.md

@@ -9,86 +9,16 @@ pinned: false
 short_description: Compression rates and compress/decompress costs
 ---
 
-# THULab Time Series Compression Benchmark
+# THULab Compression Benchmark
 
-Interactive, static leaderboard of the existing `web_compression` project results, inspired by the grouped leaderboard interface of [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval).
+The active test scope contains **137 datasets** and 85 active C++ method families. The user selected a uniform rule: exclude datasets with fewer than 128 valid values on average per numerical column/entity sequence; retain datasets at exactly 128. Compression performance does not participate in this selection. Timestamp-only medium_posts is separately ineligible because its scientific input contract excludes timestamps and admits no numerical value payload; its mean is undefined, not zero. The previous 160 scope and raw 164 archive remain preserved in Git history and the immutable result files.
 
-The Space serves published result snapshots. It does not execute compression experiments or accept raw dataset uploads. It has no runtime dependency on the project's Django server.
+`final-dataset-scope.json` and `sequence-length-audit.json` freeze the exact identities and length evidence. The active catalog retains 2 corrected-input retests pending; currently 135 dataset identities contribute numeric Overall measurements. Superseded incomplete inputs are excluded across C++, Java, Python and all dtype slices, without older fallback. Input and retest status is in `final-input-status.json`.
 
-## Update the results
+C++ selects the latest receipt-bound authority per dataset and canonical method. Its active comparison is restricted to the 85 method contract; two extra historical methods remain in raw downloads. Java/Python retain their published source profiles. Numeric dtype-specific results, terminal N/A, unsupported and retest-pending states remain distinct. Overall method cells contain only the method/configuration name; details explain coverage and retain provenance. CSV includes source hosts, support sets, timing admission and the active scope.
 
-From the source project checkout, regenerate the snapshots with:
+Compression rate remains complete compressed-frame bytes divided by original numeric binary bytes. The three shared CSV metadata files are not added to the compressed numerator. No ratio is inverted, clipped or altered. `ranking.js`, the raw data, compact data, manifests and source selection evidence are unchanged. Cross-host/campaign timing is not pooled into strict rankings; admitted timed subsets remain explicitly labeled and do not enter strict composite scores.
 
-```bash
-python3 deploy/huggingface-compressionbenchmark/export_results.py
-python3 deploy/huggingface-compressionbenchmark/apply_dataset_scope.py
-# Reapply verified measurement updates before packing the browser payload.
-python3 scripts/integrate_dbp_leaderboard.py --measurements /path/to/frozen-measurements.json --expected-sha256 VERIFIED_SHA256 --receipt /path/to/integration-receipt.json
-# Verify source identities, rebuild timing averages, and reapply the targeted retest.
-python3 scripts/update_site_column_means.py \
-  --receipt /path/to/timing-fallback-receipt.json \
-  --dbp-medium-payload analysis_outputs/column_mean_20261009/dbp_medium_posts_retest/integration_payload.json \
-  --dbp-medium-payload-sha256 e45170a955d1cd0df50dba25f9e41f24f00fc8b559e702d59188b6cfa60c99dc \
-  --dbp-medium-receipt analysis_outputs/column_mean_20261009/dbp_medium_posts_retest/measurement_receipt.json \
-  --dbp-medium-receipt-sha256 10359f6f1153655710f7ce9b59d04e5460babe5606487b7794a47ba874ef4324
-# Rebuild the complete Native86 terminal matrix. The importer verifies the
-# 13,940-pair main export, overlays the 154 current receipt-bound V9 recoveries,
-# retains the 21 independently verified server-192 pairs, and keeps host,
-# campaign, comparison contract, exact adapter, and binary identities explicit.
-python3 deploy/huggingface-compressionbenchmark/integrate_full_native86_v6.py \
-  --main-timing-proof analysis_outputs/paper164_performance_refresh_20261011/TIMING_PROVENANCE.old_main166.json
-python3 deploy/huggingface-compressionbenchmark/build_ui_payload.py
-```
-
-Publish the static UI and snapshot files together with `integrate_full_native86_v6.py`, `native86-coverage.json`, the compressed 13,940-pair terminal matrix, its support-aware dataset-result source, the current-154 V9 thin export, the recovery reconciliation, the main timing proof, and the partial independent-adjudication receipt. The manifest records their exact SHA-256 identities and metric boundaries. The compact browser payload retains the full snapshot's measurements and SHA-256 identity. The reference and dataset catalogs are independent documentation payloads and do not create benchmark measurements.
-
-Only measurements in the authoritative current project scope are exported. Missing metrics and missing coverage are displayed explicitly. Existing results do not imply independently verified round trips or uniform hardware environments unless those checks are recorded in the source.
-
-The reviewed 164-dataset scope contains 89 directly measured real datasets and 75 datasets derived from real observations. Fifteen entries have incomplete source provenance; the catalog exposes this limitation rather than silently treating them as fully sourced.
-
-## Interface
-
-- Overall and per-dataset leaderboards with C++ / Java / Python, data-type, precision, family, and method filters
-- Sortable metrics, optional columns, and CSV download of the selected ranking
-- Compression rate versus compression/decompression cost plots
-- Dataset-level method details, snapshot provenance, and metric definitions
-- Searchable 164-dataset catalog grouped into six source categories, with explicit measurement availability and source links
-- Gold 🏆 links for the 146 datasets hosted by THULab on Hugging Face; dataset-card links open the matching selection with `?scope=164&dataset=<key>#dataset`
-- Overall composite score and average dataset Rank, with 66 valid non-negative one-decimal weight combinations, including zero weights
-- Searchable Methods documentation and a 30-family operator catalog with 102 implementation-directory entries, references, I/O contracts, examples, and status
-
-The summary dataset count and Dataset coverage denominator are derived from the selected language and data scope. The catalog remains 164 datasets; a selected result view can contain fewer numeric datasets because terminal N/A or unsupported conclusions are not converted to measurements. The C++ view uses the latest receipt-bound authority for each logical dataset/method pair. A newer terminal N/A or unsupported conclusion blocks fallback to an older numeric result. Earlier rows remain preserved in the immutable snapshot and evidence files, while method details and CSV retain the selected row's source family, source comparison profile, host, binary and measurement status. The default family view selects the lowest-rate measured exact variant independently for each dataset only when every candidate for that dataset/family has the same eligible aggregate-id set. Ambiguous support sets are omitted. Switch to **Exact configuration** to inspect fixed variants and provenance.
-
-The Overall score is `a × average rate + b × normalized average compression time + c × normalized average decompression time`. It is available only when displayed methods share one exact dataset/support signature. Average Rank compares methods within each matching `(dataset, eligible aggregate-id set)` cohort, applies dataset-local time normalization and fractional ties, then averages the available ranks. Missing, N/A, failed, and unlike-support rows are omitted. Coverage and candidate counts can differ; method details expose support hashes, dataset scores, ranks and candidate counts. CSV downloads include weights, support hashes, evidence status and rank coverage. The default weights are `a=0.4, b=0.3, c=0.3`.
-
-Compression and decompression costs are calculated independently for each usable numeric column as ns/point, then reported as the arithmetic mean across those columns; each column contributes equally. Strict timing is available only when every current rate dataset for that method has admitted timing under one valid workload. A separately labeled **timed subset** is shown when the current winning rows include admitted timing for only N of M rate datasets, and only when all N rows share one exact source profile, host and timing contract. The N/M coverage is displayed inline and exported to CSV. Timed-subset values never enter strict composite scores. Finite source timings with diagnostic or unbound provenance remain unavailable and are not borrowed from superseded rows.
-
-The server-166 terminal matrix contains all `164 × 85 = 13,940` dataset/method pairs. Later independently reviewed recovery, completion and corrected-input receipts can supersede those base pair results. The combined C++ rate view does not average timings across servers or campaigns. Main-profile timing is admitted by a frozen source/build/binary/boundary proof; recovery timing remains unavailable without the same proof. Source environments and original measurement statuses remain explicit provenance fields.
-
-Java and Python rows retain their original comparison profile and use the explicit runtime host label already stored in the frozen browser payload. This metadata is never inferred from the selected UI language. The receipt-bound `medium_posts` DBP records keep their admitted timing; other imported timings remain diagnostic and unavailable to strict timing or composite scoring.
-
-## Combined C++ latest-result view
-
-`cpp-latest-view.json` is an index-only, SHA-bound selection sidecar. It stores no copied or transformed measurement values. The frozen sidecar SHA-256 is `5d2a8bb4fb5c38fbc4fa130a08b7608c7568f3959ef9dd2706f245862907f90f`; it selects 26,834 compact rows, including 24,617 admitted-timing rows from one main-server timing contract. Each selected C++ row refers to an integer index in the unchanged `ui-data.json` compact table and records its canonical family, original family, source profile, runtime, host, authority and pair status. Pair-level campaign selection is atomic, so scopes absent from a newer winning campaign are tombstoned instead of backfilled from an older campaign. The browser verifies the sidecar SHA, frozen payload SHA, source-data SHA, index bounds, uniqueness, source family, language boundary and timing-admission index before rendering. A mismatch fails closed. FreshRetailNet-50K corrected full114 remains pending and is not represented as a newer accepted campaign.
-
-The independent evidence review covers 773 archived strict-admission pairs. Its 147 non-adjudicated entries belong to the earlier 920-pair recovery bundle and retain the explicit `partial_verification_blocked` status; the site does not generalize that review to all 13,940 terminal pairs.
-
-Source project: https://github.com/xjz17/web_compression
-
-
-The timing updater verifies the input hashes, replaces existing dataset/runtime/scope rows by identity, and adds only missing rows. The measurement harness and its frozen-source instructions are in `scripts/dbp_medium_posts_retest/`; raw data and compiled binaries are excluded from Git.
-
-## Native86 accepted completion v7
-
-The independently accepted 328-pair completion closes the 502 original Native86 problem identities for rate/byte reporting. Completion timing remains diagnostic and is excluded from timing and composite rankings. Host, campaign action, binary, adapter configuration and exact eligible support are retained as separate comparison dimensions.
-
-## Operator evidence storage packaging
-
-The 41,090-row operator evidence download is published as
-`operator-byte-effect-full-union.csv.gz`; decompress it with a standard gzip
-reader to recover the original CSV bytes. The manifest and
-`OPERATOR970_BYTE_EFFECT_GZIP_PACKAGING_V1.json` record both the physical gzip
-SHA-256 and the logical decompressed CSV SHA-256. The unchanged Native V1 and
-Operator V2 receipts bind the source candidate; the gzip packaging receipt
-binds this derived manifest and preserves the accepted row semantics.
+Historical operator byte effects are retained as diagnostics. Current-scope, corrected-input operator results remain pending and must be independently accepted before replacing them. Dataset corrections are documented in `FINAL160_FORMAT_AUDIT.json`; its previous 160-dataset investigation is a historical parent-scope audit, not the active selection list.
 
+The historical `164×85=13,940` terminal matrix remains downloadable and is not a current-scope completion claim. [Parent documentation](https://huggingface.co/spaces/THULab/CompressionBenchmark/blob/799b74b93d0c1a25b7b7a085614f6a2c8e631468/README.md) preserves the previous pipeline and provenance.

app.js

@@ -1,3 +1,4 @@
+import {FINAL160_SCOPE,FINAL160_INPUT_STATUS,applyFinal160Scope,filterFinal160Catalog,final160CoverageForFamily} from './final-scope.js?v=20261013-min128-v1';
 import {WEIGHT_TENTHS,parseWeights,addRankingMetrics} from './ranking.js?v=20261011-support-matched-v1';
 import {DEFAULT_PROFILE,profileLabel,defaultColumnVisible,metricAvailability,summarizeAvailability,visibleColumns,profileScopeText,aggregateTimedSubset,strictTimingMode} from './availability.js?v=20261013-overall-availability-v3';
 const CPP_LATEST_VIEW_SHA256='5d2a8bb4fb5c38fbc4fa130a08b7608c7568f3959ef9dd2706f245862907f90f';
@@ -121,7 +122,7 @@ function renderTable() {
   const visible=visibleColumns(eligibleColumns,currentRows,state().runtime,$('show-unavailable-columns').checked,touchedColumns);
   $('leaderboard').querySelector('thead').innerHTML=`<tr><th>Position</th><th>Method / configuration</th>${visible.map(c=>`<th aria-sort="${sort.key===c.key?(sort.direction===1?'ascending':'descending'):'none'}"><button data-sort="${c.key}" title="Sort ${escape(c.label)}">${escape(c.label)}${sort.key===c.key?(sort.direction===1?' ▴':' ▾'):''}</button></th>`).join('')}<th>Dataset coverage</th></tr>`;
   const minima=Object.fromEntries(visible.map(c=>[c.key,Math.min(...currentRows.map(r=>number(r[c.key])).filter(n=>n!==null))]));
-  $('leaderboard').querySelector('tbody').innerHTML=currentRows.length?currentRows.map((r,i)=>`<tr><td class="rank numeric">${i+1}</td><td><button class="method-button" data-method="${escape(r.method)}">${escape(r.method)}</button><span class="method-family">${escape(r.family)} · ${escape(runtimeName(r.runtime))}</span></td>${visible.map(c=>`<td class="numeric ${!['compressed_bytes','original_bytes','rank_coverage','timing_column_count'].includes(c.key)&&number(r[c.key])!==null&&r[c.key]===minima[c.key]?'best':''}">${metricCell(r,c)}</td>`).join('')}<td><span class="coverage ${r.coverage===r.totalDatasets?'full':''}">${r.coverage} / ${r.totalDatasets}</span></td></tr>`).join(''):`<tr><td colspan="${visible.length+3}" class="empty-cell">No measurements match this selection. Try another runtime, data type, or dataset.</td></tr>`;
+  $('leaderboard').querySelector('tbody').innerHTML=currentRows.length?currentRows.map((r,i)=>`<tr><td class="rank numeric">${i+1}</td><td><button class="method-button" data-method="${escape(r.method)}">${escape(r.method)}</button>${activeTab==='overall'?'':`<span class="method-family">${escape(r.family)} · ${escape(runtimeName(r.runtime))}</span>`}</td>${visible.map(c=>`<td class="numeric ${!['compressed_bytes','original_bytes','rank_coverage','timing_column_count'].includes(c.key)&&number(r[c.key])!==null&&r[c.key]===minima[c.key]?'best':''}">${metricCell(r,c)}</td>`).join('')}<td><span class="coverage ${r.coverage===r.totalDatasets?'full':''}">${r.coverage} / ${r.totalDatasets}</span></td></tr>`).join(''):`<tr><td colspan="${visible.length+3}" class="empty-cell">No measurements match this selection. Try another runtime, data type, or dataset.</td></tr>`;
   for(const button of $('leaderboard').querySelectorAll('[data-sort]'))button.addEventListener('click',()=>{const key=button.dataset.sort;sort={key,direction:sort.key===key?-sort.direction:1};refresh();});
   for(const button of $('leaderboard').querySelectorAll('[data-method]'))button.addEventListener('click',()=>showMethod(button.dataset.method,true));
   $('row-count').textContent=`${currentRows.length} ${state().mode==='family'?'method families':'method configurations'} · sorted by ${cols.find(c=>c.key===sort.key)?.label??sort.key}`;
@@ -136,7 +137,7 @@ function refresh() {
   $('precision').disabled=$('dtype').value!=='float';
   const selectedDataset=state().dataset,selectedHasMeasurements=selectedDataset==='all'||records.some(r=>r.dataset===selectedDataset),columnCoverageNote='Compress and Decompress times use equal-column arithmetic means when every selected dataset has column timings. If a method has aggregate-only timings in the selection, its dataset ns/point values are averaged with equal weight per dataset. Timing basis is shown in method details and CSV. Column coverage may differ by method.';
   if(!selectedHasMeasurements) {
-    $('results-note').textContent=`No published measurements are available for this catalog dataset. It is excluded from rankings rather than treated as zero. ${columnCoverageNote}`;
+    $('results-note').textContent=FINAL160_INPUT_STATUS.pending_datasets.some(item=>item.dataset_id===selectedDataset)?'Corrected-input benchmark pending. Old incomplete input measurements are preserved in the historical snapshot and excluded from current rankings. ': `No published measurements are available for this catalog dataset. It is excluded from rankings rather than treated as zero. ${columnCoverageNote}`;
   } else if(scopeKey()==='float'&&!records.some(r=>r.scope==='float')) {
     // The exporter must publish an explicit all-float scope; never combine
     // preaggregated fixed/non-fixed metrics and call it an all-float result.
@@ -180,8 +181,12 @@ function showMethod(method,focus=false) {
   if(!row){selectedMethod=null;detail.hidden=true;return;}selectedMethod=method;
   const meta=methods.get(method)??methods.get(row.source[0].configuration), pipeline=meta?.operator_pipeline;
   const source=row.source.slice().sort((a,b)=>a.dataset.localeCompare(b.dataset));
+  const supportDecisions=row.runtime==='cpp'?final160CoverageForFamily(cppLatestView.cpp.pair_ledger,row.family):[];
+  const countSupport=predicate=>supportDecisions.filter(predicate).length;
+  const supportNote=supportDecisions.length?`C++ test scope: ${supportDecisions.length} dataset identities; ${countSupport(item=>item.status==='numeric'&&item.scopes.includes('overall'))} numeric Overall, ${countSupport(item=>item.status==='numeric'&&!item.scopes.includes('overall'))} numeric type-specific only, ${countSupport(item=>item.status==='not_applicable')} not applicable, ${countSupport(item=>item.status==='unsupported')} unsupported, ${countSupport(item=>item.status==='replacement_input_pending')} corrected-input retests pending. Type-specific results are available through the data-type filter; they are not missing experiments.`:'';
+
   const strictCompress=metricCell(row,cols.find(column=>column.key==='compression_time')),strictDecompress=metricCell(row,cols.find(column=>column.key==='decompression_time')),subsetCompress=metricCell(row,cols.find(column=>column.key==='compression_time_timed_subset')),subsetDecompress=metricCell(row,cols.find(column=>column.key==='decompression_time_timed_subset'));
-  detail.innerHTML=`<div class="detail-head"><div><h2 id="detail-title">${escape(method)}</h2><p class="muted">${escape(row.family)} · ${escape(runtimeName(row.runtime))} · ${row.coverage} measured datasets</p></div><button id="close-detail" class="quiet-button">Close details</button></div><div class="detail-grid"><div><strong>${fmt(row.compression_rate,4)}</strong><span>average compression rate</span></div><div><strong>${strictCompress}</strong><span>strict Compress ns/point</span></div><div><strong>${strictDecompress}</strong><span>strict Decompress ns/point</span></div><div><strong>${subsetCompress}</strong><span>Compress ns/point · timed subset</span></div><div><strong>${subsetDecompress}</strong><span>Decompress ns/point · timed subset</span></div><div><strong>${row.coverage} / ${row.totalDatasets}</strong><span>dataset coverage</span></div></div>${pipeline?`<p class="source-code">Interpretive operator mapping: ${escape(typeof pipeline==='string'?pipeline:JSON.stringify(pipeline))}</p>`:''}<p class="muted">Strict timing average: ${escape(timingLabel(row.timing_aggregation))}. Timed-subset values are shown only when every included timing row has one exact source profile, host, and timing contract; they never enter strict composite scores. Dataset metrics below retain the winning row's source family, profile, and host.</p><div class="detail-table"><table><thead><tr><th>Dataset</th><th>Configuration</th><th>Source family</th><th>Rate ↓</th><th>Compress ns/point ↓</th><th>Decompress ns/point ↓</th><th>Timing basis</th><th>Measurement evidence</th><th>Timing profile</th><th>Selected source profile / host</th><th>Runtime / binary</th><th>Support set</th><th>Original bytes</th><th>Compressed bytes</th><th>Dataset score ↓</th><th>Dataset Rank ↓</th><th>Rank candidates</th></tr></thead><tbody>${source.map(r=>`<tr><td>${escape(r.dataset)}</td><td>${escape(r.configuration)}</td><td>${escape(r.source_family??r.family)}</td><td class="numeric">${fmt(r.compression_rate,4)}</td><td class="numeric">${metricCell(r,cols.find(column=>column.key==='compression_time'))}</td><td class="numeric">${metricCell(r,cols.find(column=>column.key==='decompression_time'))}</td><td>${escape(timingLabel(r.timing_aggregation))}</td><td>${escape(r.measurement_status)}</td><td>${escape(r.timing_profile_status)}</td><td class="source-code">${escape(r.source_comparison_profile??r.runtime)} / ${escape(r.source_execution_host??'source snapshot')}</td><td class="source-code">${escape(r.source_runtime)} / ${escape(String(r.binary_sha256).slice(0,16))}</td><td class="source-code">${escape(String(r.eligible_column_set_sha256).slice(0,16))}</td><td class="numeric">${fmt(r.original_bytes,0)}</td><td class="numeric">${fmt(r.compressed_bytes,0)}</td><td class="numeric">${metricCell(r,{key:'dataset_score',digits:4})}</td><td class="numeric">${metricCell(r,{key:'dataset_rank',digits:2})}</td><td class="numeric">${fmt(r.dataset_candidate_count,0)}</td></tr>`).join('')}</tbody></table></div>`;
+  detail.innerHTML=`<div class="detail-head"><div><h2 id="detail-title">${escape(method)}</h2><p class="muted">${escape(row.family)} · ${escape(runtimeName(row.runtime))} · ${row.coverage} measured datasets</p></div><button id="close-detail" class="quiet-button">Close details</button></div><div class="detail-grid"><div><strong>${fmt(row.compression_rate,4)}</strong><span>average compression rate</span></div><div><strong>${strictCompress}</strong><span>strict Compress ns/point</span></div><div><strong>${strictDecompress}</strong><span>strict Decompress ns/point</span></div><div><strong>${subsetCompress}</strong><span>Compress ns/point · timed subset</span></div><div><strong>${subsetDecompress}</strong><span>Decompress ns/point · timed subset</span></div><div><strong>${row.coverage} / ${row.totalDatasets}</strong><span>dataset coverage</span></div></div>${pipeline?`<p class="source-code">Interpretive operator mapping: ${escape(typeof pipeline==='string'?pipeline:JSON.stringify(pipeline))}</p>`:''}${supportNote?`<p class="notice">${escape(supportNote)}</p>`:''}<p class="muted">Strict timing average: ${escape(timingLabel(row.timing_aggregation))}. Timed-subset values are shown only when every included timing row has one exact source profile, host, and timing contract; they never enter strict composite scores. Dataset metrics below retain the winning row's source family, profile, and host.</p><div class="detail-table"><table><thead><tr><th>Dataset</th><th>Configuration</th><th>Source family</th><th>Rate ↓</th><th>Compress ns/point ↓</th><th>Decompress ns/point ↓</th><th>Timing basis</th><th>Measurement evidence</th><th>Timing profile</th><th>Selected source profile / host</th><th>Runtime / binary</th><th>Support set</th><th>Original bytes</th><th>Compressed bytes</th><th>Dataset score ↓</th><th>Dataset Rank ↓</th><th>Rank candidates</th></tr></thead><tbody>${source.map(r=>`<tr><td>${escape(r.dataset)}</td><td>${escape(r.configuration)}</td><td>${escape(r.source_family??r.family)}</td><td class="numeric">${fmt(r.compression_rate,4)}</td><td class="numeric">${metricCell(r,cols.find(column=>column.key==='compression_time'))}</td><td class="numeric">${metricCell(r,cols.find(column=>column.key==='decompression_time'))}</td><td>${escape(timingLabel(r.timing_aggregation))}</td><td>${escape(r.measurement_status)}</td><td>${escape(r.timing_profile_status)}</td><td class="source-code">${escape(r.source_comparison_profile??r.runtime)} / ${escape(r.source_execution_host??'source snapshot')}</td><td class="source-code">${escape(r.source_runtime)} / ${escape(String(r.binary_sha256).slice(0,16))}</td><td class="source-code">${escape(String(r.eligible_column_set_sha256).slice(0,16))}</td><td class="numeric">${fmt(r.original_bytes,0)}</td><td class="numeric">${fmt(r.compressed_bytes,0)}</td><td class="numeric">${metricCell(r,{key:'dataset_score',digits:4})}</td><td class="numeric">${metricCell(r,{key:'dataset_rank',digits:2})}</td><td class="numeric">${fmt(r.dataset_candidate_count,0)}</td></tr>`).join('')}</tbody></table></div>`;
   detail.hidden=false;$('close-detail').addEventListener('click',()=>{detail.hidden=true;selectedMethod=null;});
   if(focus){detail.scrollIntoView({block:'nearest',behavior:'auto'});detail.focus({preventScroll:true});}
 }
@@ -205,8 +210,8 @@ function renderChart() {
 function csv() {
   const quote=v=>{let text=String(v??'');if(typeof v==='string'&&/^[=+@-]/.test(text))text="'"+text;return `"${text.replace(/"/g,'""')}"`;};
   const weights=parseWeights($('weights').value);
-  const columns=['position','method','comparison_profile','execution_host','scope','row_mode','selected_dataset','sort_metric','sort_direction','average_compression_rate','overall_compression_rate','compress_ns_per_point','decompress_ns_per_point','weight_a','weight_b','weight_c','normalized_compress','normalized_decompress','composite_score','average_rank','rank_dataset_coverage','normalization_reference_rows','compress_min','compress_max','decompress_min','decompress_max','normalization','rank_definition','dataset_coverage','datasets_in_scope','selected_configuration_by_dataset','source_profile_by_dataset','binary_sha256_by_dataset','eligible_column_set_sha256_by_dataset','measurement_status_by_dataset','timing_profile_status_by_dataset','timing_column_count','timing_aggregation','timed_subset_compress_ns_per_point','timed_subset_decompress_ns_per_point','timed_subset_dataset_coverage','timed_subset_dataset_denominator','timed_subset_status','timed_subset_source_profiles','timed_subset_source_hosts','selected_source_family_by_dataset','selected_source_comparison_profile_by_dataset','selected_source_host_by_dataset','strict_timing_reason','composite_reason'];
-  const body=currentRows.map((r,i)=>[i+1,r.method,r.runtime,runtimeHost(r.runtime),scopeKey(),state().mode,state().dataset,sort.key,sort.direction===1?'ascending':'descending',r.compression_rate,r.overall_rate,r.compression_time,r.decompression_time,...weights.map(w=>w.toFixed(1)),r.normalized_compression_time,r.normalized_decompression_time,r.composite_score,r.average_rank,r.rank_coverage,r.normalization_reference_rows,r.encode_min,r.encode_max,r.decode_min,r.decode_max,'min-max over all measured rows in selected language/scope/row-mode before search and visibility filters','mean per-dataset composite-score fractional rank; per-dataset min-max times',r.coverage,r.totalDatasets,JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.configuration]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_runtime]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.binary_sha256]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.eligible_column_set_sha256]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.measurement_status]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.timing_profile_status]))),r.timing_column_count,r.timing_aggregation,r.compression_time_timed_subset,r.decompression_time_timed_subset,r.timing_subset_coverage,r.timing_subset_denominator,r.timing_subset_status,JSON.stringify(r.timing_subset_source_profiles),JSON.stringify(r.timing_subset_hosts),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_family??s.family]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_comparison_profile??s.runtime]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_execution_host??'source snapshot']))),metricAvailability(r,'compression_time').code,metricAvailability(r,'composite_score').code].map(quote).join(','));
+  const columns=['position','method','comparison_profile','execution_host','scope','row_mode','selected_dataset','sort_metric','sort_direction','average_compression_rate','overall_compression_rate','compress_ns_per_point','decompress_ns_per_point','weight_a','weight_b','weight_c','normalized_compress','normalized_decompress','composite_score','average_rank','rank_dataset_coverage','normalization_reference_rows','compress_min','compress_max','decompress_min','decompress_max','normalization','rank_definition','dataset_coverage','datasets_in_scope','selected_configuration_by_dataset','source_profile_by_dataset','binary_sha256_by_dataset','eligible_column_set_sha256_by_dataset','measurement_status_by_dataset','timing_profile_status_by_dataset','timing_column_count','timing_aggregation','timed_subset_compress_ns_per_point','timed_subset_decompress_ns_per_point','timed_subset_dataset_coverage','timed_subset_dataset_denominator','timed_subset_status','timed_subset_source_profiles','timed_subset_source_hosts','selected_source_family_by_dataset','selected_source_comparison_profile_by_dataset','selected_source_host_by_dataset','strict_timing_reason','composite_reason','final_dataset_scope','input_version_gate','pending_replacement_datasets'];
+  const body=currentRows.map((r,i)=>[i+1,r.method,r.runtime,runtimeHost(r.runtime),scopeKey(),state().mode,state().dataset,sort.key,sort.direction===1?'ascending':'descending',r.compression_rate,r.overall_rate,r.compression_time,r.decompression_time,...weights.map(w=>w.toFixed(1)),r.normalized_compression_time,r.normalized_decompression_time,r.composite_score,r.average_rank,r.rank_coverage,r.normalization_reference_rows,r.encode_min,r.encode_max,r.decode_min,r.decode_max,'min-max over all measured rows in selected language/scope/row-mode before search and visibility filters','mean per-dataset composite-score fractional rank; per-dataset min-max times',r.coverage,r.totalDatasets,JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.configuration]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_runtime]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.binary_sha256]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.eligible_column_set_sha256]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.measurement_status]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.timing_profile_status]))),r.timing_column_count,r.timing_aggregation,r.compression_time_timed_subset,r.decompression_time_timed_subset,r.timing_subset_coverage,r.timing_subset_denominator,r.timing_subset_status,JSON.stringify(r.timing_subset_source_profiles),JSON.stringify(r.timing_subset_hosts),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_family??s.family]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_comparison_profile??s.runtime]))),JSON.stringify(Object.fromEntries(r.source.map(s=>[s.dataset,s.source_execution_host??'source snapshot']))),metricAvailability(r,'compression_time').code,metricAvailability(r,'composite_score').code,FINAL160_INPUT_STATUS.scope_id,'not_quarantined_by_current_input_audit',JSON.stringify(FINAL160_INPUT_STATUS.pending_datasets.map(item=>item.dataset_id))].map(quote).join(','));
   const url=URL.createObjectURL(new Blob(['\ufeff'+[columns.join(','),...body].join('\r\n')],{type:'text/csv;charset=utf-8'}));const a=document.createElement('a');a.href=url;a.download=`THULab-compression-${state().runtime}-${scopeKey()}-${state().dataset}.csv`;a.click();setTimeout(()=>URL.revokeObjectURL(url),1000);
 }
 function safeHttpUrl(value) {
@@ -266,7 +271,7 @@ function renderOperatorEffect() {
   if(operatorEffectError){status.textContent='Verified byte-effect results could not be loaded.';body.innerHTML=`<tr><td colspan="7" class="empty-cell">${escape(operatorEffectError.message)}</td></tr>`;return;}
   if(!operatorEffect){status.textContent='Loading verified byte effects…';return;}
   const rows=operatorEffect.rows||[],audit=operatorEffect.audit||{};
-  status.textContent=`${rows.length} omitted operators · ${countFmt(audit.admitted_ok_row_count||0)} admitted rows from the exact ${countFmt(audit.input_row_count||0)}-row union · timing unavailable`;
+  status.textContent=`Historical input-version diagnostic. Current-scope, corrected-input operator replacement is pending. ${rows.length} omitted operators · ${countFmt(audit.admitted_ok_row_count||0)} admitted rows from the exact ${countFmt(audit.input_row_count||0)}-row union · timing unavailable`;
   body.innerHTML=rows.length?rows.map(row=>`<tr><td>${escape(row.operator)}</td><td class="numeric">${fmt(row.mean_omission_to_baseline_ratio,3)}×</td><td class="numeric ${number(row.mean_byte_change_percent)>0?'operator-effect-cost':'operator-effect-saving'}">${number(row.mean_byte_change_percent)>0?'+':''}${fmt(row.mean_byte_change_percent,1)}%</td><td class="numeric">${fmt(row.mean_baseline_to_original_ratio,3)}</td><td class="numeric">${fmt(row.mean_omission_to_original_ratio,3)}</td><td class="numeric"><span class="coverage">${countFmt(row.dataset_count)}</span></td><td class="numeric">${countFmt(row.row_count)}</td></tr>`).join(''):'<tr><td colspan="7" class="empty-cell">No admitted matched-omission rows.</td></tr>';
 }
 async function loadOperatorEffect() {
@@ -323,7 +328,7 @@ function datasetKey(entry) { return String(entry?.key??entry?.dataset??''); }
 function datasetDisplayName(key) { const entry=datasets.get(key);return entry?.label&&entry.label!==key?`${entry.label} (${key})`:key; }
 function datasetEntries() {
   const source=Array.isArray(datasetCatalog?.datasets)&&datasetCatalog.datasets.length?datasetCatalog.datasets:[...(snapshot?.datasets||[])];
-  const unique=new Map();for(const entry of source){const key=datasetKey(entry);if(key)unique.set(key,entry);}return [...unique.values()];
+  const unique=new Map();for(const entry of source){const key=datasetKey(entry);if(key&&FINAL160_SCOPE.datasets.includes(key))unique.set(key,entry);}return [...unique.values()];
 }
 function datasetCategory(entry) { return String(entry.category_label??entry.category??'Other'); }
 function datasetSearchText(entry) { return [datasetKey(entry),entry.label,entry.category,entry.category_label,entry.source,entry.dataset_url,thulabDatasetUrl(entry)?'THULab Hugging Face hosted':''].join(' ').toLowerCase(); }
@@ -353,24 +358,24 @@ function renderDatasetCatalog() {
 }
 function renderDatasetEntry(entry,isMeasured) {
   const key=datasetKey(entry),label=publicText(entry.label,key),rows=number(entry.row_count),payload=humanBytes(entry.size_bytes),metadata=[rows===0?'Empty dataset':rows!==null?`${countFmt(rows)} rows`:'',number(entry.column_count)!==null?`${countFmt(entry.column_count)} columns`:'',number(entry.numeric_column_count)!==null?`${countFmt(entry.numeric_column_count)} numeric`:'',payload?`${payload} numeric payload`:'' ].filter(Boolean),sourceUrl=safeHttpUrl(entry.source_url),datasetUrl=safeHttpUrl(entry.dataset_url),thulabUrl=thulabDatasetUrl(entry),source=publicText(entry.source,'Source'),snapshotDiff=entry.published_result_row_count_matches_catalog===false;
-  return `<article class="dataset-entry ${isMeasured?'measured':'pending'} ${state().dataset===key?'selected':''}"><button type="button" data-dataset-key="${escape(key)}"><span><strong>${escape(label)}</strong>${label!==key?`<small>${escape(key)}</small>`:''}</span><span class="dataset-state">${isMeasured?'Measured':'No published measurements'}</span></button>${thulabUrl?`<div class="dataset-affiliations">${thulabDatasetBadge(entry,key)}</div>`:''}${metadata.length?`<p>${metadata.map(escape).join(' · ')}</p>`:''}${snapshotDiff?'<p class="dataset-snapshot-note">Published measurements use an earlier prepared snapshot.</p>':''}<div class="dataset-links">${sourceUrl?`<a href="${escape(sourceUrl)}" target="_blank" rel="noopener noreferrer">${escape(source)} ↗</a>`:entry.source?`<span class="dataset-source">${escape(source)}</span>`:''}${datasetUrl&&datasetUrl!==sourceUrl&&!thulabUrl?`<a href="${escape(datasetUrl)}" target="_blank" rel="noopener noreferrer">Dataset files ↗</a>`:''}</div></article>`;
+  return `<article class="dataset-entry ${isMeasured?'measured':'pending'} ${state().dataset===key?'selected':''}"><button type="button" data-dataset-key="${escape(key)}"><span><strong>${escape(label)}</strong>${label!==key?`<small>${escape(key)}</small>`:''}</span><span class="dataset-state">${isMeasured?'Measured':'No published measurements'}</span></button>${thulabUrl?`<div class="dataset-affiliations">${thulabDatasetBadge(entry,key)}</div>`:''}${metadata.length?`<p>${metadata.map(escape).join(' · ')}</p>`:''}${entry.current_input_issue?`<p class="dataset-snapshot-note">Corrected-input retest pending: ${escape(entry.current_input_issue)}</p>`:''}${snapshotDiff?'<p class="dataset-snapshot-note">Published measurements use an earlier prepared snapshot.</p>':''}<div class="dataset-links">${sourceUrl?`<a href="${escape(sourceUrl)}" target="_blank" rel="noopener noreferrer">${escape(source)} ↗</a>`:entry.source?`<span class="dataset-source">${escape(source)}</span>`:''}${datasetUrl&&datasetUrl!==sourceUrl&&!thulabUrl?`<a href="${escape(datasetUrl)}" target="_blank" rel="noopener noreferrer">Dataset files ↗</a>`:''}</div></article>`;
 }
 async function loadDatasetCatalog() {
   try {
     const response=await fetch('./dataset-catalog.json?v=20261013-corrected-five-v1',{cache:'no-cache'});if(!response.ok)throw new Error(`Dataset catalog request returned ${response.status}.`);
-    const catalog=await response.json();if(!catalog||!Array.isArray(catalog.datasets)||!catalog.summary)throw new Error('Dataset catalog has an invalid schema.');datasetCatalog=catalog;
+    const catalog=await response.json();if(!catalog||!Array.isArray(catalog.datasets)||!catalog.summary)throw new Error('Dataset catalog has an invalid schema.');datasetCatalog=filterFinal160Catalog(catalog);
   } catch(error) { datasetCatalogError=error instanceof Error?error:new Error('Dataset catalog is unavailable.'); }
   if(snapshot){populateDatasetControls();renderDatasetCatalog();renderAbout();}
 }
 function renderAbout() {
   const b=snapshot.benchmark||{},s=snapshot.source||{},c=snapshot.coverage||{};
-  const notes=[b.scope_note,b.aggregation_note,b.selection_note,...(b.notes||[]),...(b.measurement_boundaries||[])].filter(Boolean);
+  const notes=['The active test scope contains 137 dataset identities after uniformly excluding mean numerical-sequence lengths below 128; timestamp-only medium_posts is separately ineligible because it has no admitted scientific numeric payload. The previous 160 identity scope and all old inputs/results are preserved in the archive. FreshRetailNet-LT and Kaggle Web Traffic old incomplete inputs are quarantined pending new accepted results. Historical downloads retain the original scope and versions.',b.aggregation_note,b.selection_note,...(b.notes||[]),...(b.measurement_boundaries||[])].filter(Boolean);
   $('methodology').innerHTML=notes.map(n=>`<p class="notice">${escape(n)}</p>`).join('');
   const definitions=b.metric_definitions||{};if(Object.keys(definitions).length)$('methodology').innerHTML+=`<dl class="definitions">${Object.entries(definitions).map(([k,v])=>`<div><dt>${escape(k.replaceAll('_',' '))}</dt><dd>${escape(typeof v==='string'?v:JSON.stringify(v))}</dd></div>`).join('')}</dl>`;
   const exclusions=c.exclusions||c.excluded_datasets||snapshot.exclusions||[];
   const catalogCount=number(datasetCatalog?.summary?.dataset_count??c.catalog_dataset_count)??datasetEntries().length,measuredCount=number(datasetCatalog?.summary?.published_result_dataset_count??datasetCatalog?.summary?.published_measurement_dataset_count??datasetCatalog?.summary?.measured_dataset_count??c.measured_dataset_count)??new Set(records.map(r=>r.dataset)).size;
   const terminal=c.native86_terminal_coverage||{};
-  const info={ 'Snapshot exported':snapshot.exported_at||s.exported_at||'Unavailable in snapshot','Dataset catalog':`${catalogCount} datasets; ${measuredCount} currently have published measurements and participate in rankings`,'Native86 server-166 terminal matrix':terminal.expected_pair_count?`${terminal.observed_pair_count} / ${terminal.expected_pair_count} dataset-method pairs; ${terminal.numeric_pair_count??0} contain published numeric aggregates, ${terminal.not_applicable_pair_count??0} are explicit N/A, ${terminal.problem_pair_count??0} ended with a failure status`:'See manifest and terminal coverage download','Dataset scope generated':s.generated_at||'Unavailable in snapshot', 'Source checkout':s.git_commit||'Unavailable in snapshot','Source files':`${s.source_files?.length??0} metadata inputs and ${s.result_csv_files_used?.length??0} result CSVs (hashes in manifest)`,'Result entries across scopes':`${records.length} (scope slices can reuse a measurement)`,'Worktree source state':s.worktree_dirty_inputs?.length?'Includes current local changes; input file hashes identify the published snapshot.':'See manifest for source file hashes.','Runtime environment':b.hardware||'Hardware details are unavailable in this exported result snapshot.','Lossless evidence':b.roundtrip_evidence||'Lossless-design method filter from the source project; per-run round-trip verification is unavailable here.'};
+  const info={ 'Snapshot exported':snapshot.exported_at||s.exported_at||'Unavailable in snapshot','Dataset catalog':`${catalogCount} datasets; ${measuredCount} currently have published measurements and participate in rankings`,'Historical 164-dataset Native85 terminal matrix':terminal.expected_pair_count?`${terminal.observed_pair_count} / ${terminal.expected_pair_count} dataset-method pairs; ${terminal.numeric_pair_count??0} contain published numeric aggregates, ${terminal.not_applicable_pair_count??0} are explicit N/A, ${terminal.problem_pair_count??0} ended with a failure status`:'See manifest and terminal coverage download','Dataset scope generated':s.generated_at||'Unavailable in snapshot', 'Source checkout':s.git_commit||'Unavailable in snapshot','Source files':`${s.source_files?.length??0} metadata inputs and ${s.result_csv_files_used?.length??0} result CSVs (hashes in manifest)`,'Result entries across scopes':`${records.length} (scope slices can reuse a measurement)`,'Worktree source state':s.worktree_dirty_inputs?.length?'Includes current local changes; input file hashes identify the published snapshot.':'See manifest for source file hashes.','Runtime environment':b.hardware||'Hardware details are unavailable in this exported result snapshot.','Lossless evidence':b.roundtrip_evidence||'Lossless-design method filter from the source project; per-run round-trip verification is unavailable here.'};
   $('provenance').innerHTML=`<table class="provenance-table"><tbody>${Object.entries(info).map(([k,v])=>`<tr><th>${escape(k)}</th><td class="source-code">${escape(v)}</td></tr>`).join('')}</tbody></table>${exclusions.length?`<h3>Excluded or unavailable results</h3><p class="source-code">${escape(typeof exclusions==='string'?exclusions:JSON.stringify(exclusions))}</p>`:''}`;
 }
 function decodeCompactRecord(row,index) {
@@ -408,6 +413,7 @@ function applyCppLatestView() {
 }
 function init() {
   applyCppLatestView();
+  snapshot=applyFinal160Scope(snapshot);
   for(const m of snapshot.methods||[]) { const method=typeof m==='string'?{name:m}:m;methods.set(method.name??method.method,method);for(const c of method.configurations||[])methods.set(c.algorithm_variant,{...method,name:c.algorithm_variant,family:method.name}); }
   for(const d of snapshot.datasets||[])datasets.set(d.key??d.dataset,d);
   records=(snapshot.dataset_results||snapshot.records||[]).map(normalizeRecord).filter(r=>r.method&&r.dataset&&r.compression_rate>0);

dataset-catalog.json

@@ -28,7 +28,8 @@
       "source": "Kaggle",
       "source_order": 1,
       "source_url": "https://www.kaggle.com/datasets/dakshbhalala/uci-air-quality-dataset",
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+      "mean_valid_values_per_numerical_sequence": 4207608.0
     },
     {
       "category": "finance_commerce",
@@ -4828,7 +4188,8 @@
       "source": "Hugging Face",
       "source_order": 165,
       "source_url": "https://huggingface.co/datasets/czl/xinyi_public_gym",
-      "value_cell_count": 555933
+      "value_cell_count": 555933,
+      "mean_valid_values_per_numerical_sequence": 91608.0
     },
     {
       "category": "finance_commerce",
@@ -4858,7 +4219,8 @@
       "source": "Hugging Face",
       "source_order": 166,
       "source_url": "https://huggingface.co/datasets/Torch-Trade/xrpusdt_spot_1m_05_2021_to_03_2026/blob/f6e36ee337c0c61f1b454ebe6baba4ee6900c242/README.md",
-      "value_cell_count": 12708000
+      "value_cell_count": 12708000,
+      "mean_valid_values_per_numerical_sequence": 2541600.0
     },
     {
       "category": "finance_commerce",
@@ -4888,37 +4250,8 @@
       "source": "Hugging Face",
       "source_order": 167,
       "source_url": "https://huggingface.co/datasets/czl/yongchun_public_gym",
-      "value_cell_count": 212130
-    },
-    {
-      "category": "finance_commerce",
-      "category_label": "Finance & Commerce",
-      "cohort": "thulab",
-      "cohort_label": "THULab dataset collection",
-      "column_count": 45,
-      "controlled_test_data": false,
-      "dataset_url": "https://huggingface.co/datasets/THULab/zillow_viewer",
-      "has_published_results": true,
-      "key": "zillow_viewer",
-      "label": "zillow_viewer",
-      "measurement_eligibility": "eligible",
-      "numeric_column_count": 44,
-      "origin_class": "derived_from_real",
-      "origin_confidence": "medium",
-      "preparation_status": "ready",
-      "production_context": "unspecified_or_production_observational",
-      "provenance_status": "source_supported",
-      "published_result_row_count": null,
-      "published_result_row_count_matches_catalog": null,
-      "published_result_scope": "published_measurements",
-      "row_count": 2878384,
-      "shared_metadata_bytes": 122237885,
-      "size_basis": "native_numeric_bytes_excluding_validity_and_metadata",
-      "size_bytes": 67041832,
-      "source": "Hugging Face",
-      "source_order": 169,
-      "source_url": "https://huggingface.co/datasets/misikoff/zillow-viewer",
-      "value_cell_count": 15886651
+      "value_cell_count": 212130,
+      "mean_valid_values_per_numerical_sequence": 70313.0
     }
   ],
   "excluded": [
@@ -4945,10 +4278,118 @@
       "origin_class": "simulated_synthetic",
       "reason": "TIME v4 explicitly describes five years of simulated delivery records; both customer and location tables are aggregations of that simulated transaction source.",
       "source_url": "https://arxiv.org/html/2602.12147v4"
+    },
+    {
+      "key": "jovian_lightning",
+      "reason": "Outside user-authorized final160 scope; historical data preserved."
+    },
+    {
+      "key": "macroecon",
+      "reason": "Outside user-authorized final160 scope; historical data preserved."
+    },
+    {
+      "key": "nasa_maven_kp_insitu",
+      "reason": "Outside user-authorized final160 scope; historical data preserved."
+    },
+    {
+      "key": "weather_data_NC",
+      "reason": "Outside user-authorized final160 scope; historical data preserved."
+    },
+    {
+      "key": "medium_posts",
+      "reason": "No admitted scientific numeric payload; timestamp coordinates are excluded by the source role contract."
+    },
+    {
+      "key": "africa_senegal_mauritania_crops_spi",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "car_parts",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "dmi-aarhus-weather-data",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "freshretailnet_50k",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "github_top_projects",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "globalstar_fleet_data",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "hospital-monash",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "housing_inventory",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "kaggle_web_traffic_weekly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "magseven_ml_ready_high_frequency_anomaly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "niederschlag_station_4091_bavaria_germany",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "nn5_weekly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "solar_weekly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "timeseries-finance-ETF",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "timeseries_finance_etf_missing",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "tourism_quarterly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "tourism_yearly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "traffic_weekly",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "traqq_weekly_activity",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "trending_stocks_yahoo_finance",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "us_stock_financial_ratios",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
+    },
+    {
+      "key": "zillow_viewer",
+      "reason": "User-selected uniform rule: mean valid values per numerical sequence <128. Historical evidence preserved."
     }
   ],
   "schema_version": 1,
-  "scope_id": "paper169-realdata-scope-20261008",
+  "scope_id": "min128_20261013_v1",
   "source": {
     "field_boundaries": {
       "column_count": "Prepared-scope source_inventory_column_count.",
@@ -4989,56 +4430,58 @@
     ],
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     "scope_formula": "18+151=169",
-    "source_scope_sha256": "7ec5714939750c0551371d52e593685e3c4226681a4283af0a4d5b8abea64cfd"
+    "source_scope_sha256": "7ec5714939750c0551371d52e593685e3c4226681a4283af0a4d5b8abea64cfd",
+    "final_scope_sha256": "b56f9bd610e0bf63ab50d719ce70cf92ddc1b79e6b30fd2c6e2f707944b2ba72",
+    "sequence_length_audit_sha256": "9c340b8f314984881230f0bf3122f45fde64b885ce3e26baf1ec3df1f2540df4"
   },
   "summary": {
-    "catalog_only_dataset_count": 0,
+    "catalog_only_dataset_count": 2,
     "categories": [
       {
-        "count": 36,
+        "count": 29,
         "key": "environment_space",
         "label": "Environment & Space"
       },
       {
-        "count": 15,
+        "count": 13,
         "key": "health_society",
         "label": "Health & Society"
       },
       {
-        "count": 14,
+        "count": 11,
         "key": "web_computing",
         "label": "Web & Computing"
       },
       {
-        "count": 15,
+        "count": 14,
         "key": "transport_mobility",
         "label": "Transport & Mobility"
       },
       {
-        "count": 60,
+        "count": 47,
         "key": "finance_commerce",
         "label": "Finance & Commerce"
       },
       {
-        "count": 24,
+        "count": 23,
         "key": "energy_industry",
         "label": "Energy & Industry"
       }
     ],
-    "dataset_count": 164,
+    "dataset_count": 137,
     "empty_dataset_count": 0,
-    "legacy_count": 18,
-    "measurement_note": "The reviewed scope contains 89 real-measured and 75 derived-from-real datasets; 15 have incomplete source provenance. Availability varies by method, runtime, and data type; missing measurements remain unavailable.",
+    "legacy_count": 17,
+    "measurement_note": "Test scope: 137 dataset identities after the uniform mean numerical-sequence length >=128 filter. 2 incomplete historical input versions are excluded pending corrected-input results; 135 currently contribute numeric Overall results.",
     "origin_classes": {
-      "derived_from_real": 75,
-      "real_measured": 89
+      "real_measured": 76,
+      "derived_from_real": 61
     },
     "provenance_statuses": {
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-      "source_supported": 149
+      "source_supported": 125,
+      "incomplete": 12
     },
-    "published_measurement_dataset_count": 164,
-    "published_result_dataset_count": 164,
-    "thulab_count": 146
+    "published_measurement_dataset_count": 135,
+    "published_result_dataset_count": 135,
+    "thulab_count": 120
   }
 }

final-dataset-scope.json

@@ -0,0 +1,598 @@
+{
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+    "PEMS-BAY",
+    "RealEstate1",
+    "RealEstate2",
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+    "SpatialEpiBench",
+    "Telco",
+    "TrainsUK2",
+    "Uberlandia",
+    "Wins",
+    "YaleLanguages",
+    "ahr999-dataset",
+    "ast_spacemobile_fleet_data",
+    "australia_solar",
+    "australian_electricity_demand",
+    "auto_production_sf",
+    "azure2019_u",
+    "bitcoin",
+    "bitcoin_price_timeseries",
+    "coastal_t_s",
+    "commodity_import",
+    "commodity_production",
+    "covid_deaths-monash",
+    "cphl",
+    "crypto",
+    "crypto_data",
+    "current_velocity",
+    "deep-space-probes",
+    "dst-index",
+    "ecdc_covid",
+    "eea_pm25_forecasting",
+    "electricity_demand",
+    "electricity_load_2011_2014",
+    "electricity_production",
+    "enedis-with-holidays",
+    "epf_electricity_price",
+    "eweld_load",
+    "exchange_rate",
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+    "finland_traffic",
+    "forex_factory_calendar",
+    "fred_md",
+    "freshretailnet_lt",
+    "fulus_lyd_rates_2025",
+    "geomagnetic_kp_index",
+    "global_influenza",
+    "global_price",
+    "gluco_tsfm_benchmark",
+    "goes_omni_electron_flux_forecasting",
+    "greatplains_multisource_2000_2024",
+    "hourly-stock-data-2023",
+    "hourly_electricity_demand_brazil",
+    "indian_stock_hourly_2017_2021",
+    "job_claims",
+    "jolts",
+    "kaggle_web_traffic",
+    "kdd_cup_2018",
+    "knowable",
+    "kraken-trading-data",
+    "kuiper_fleet_data",
+    "london_smart_meters",
+    "m2oe2_timeseries",
+    "meteoblue",
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+    "multi_model_trading_data",
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+    "ne_china_wind",
+    "nifty50_market_regime",
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+    "oil_price",
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+    "online_retail_2_uci",
+    "ooni_censorship_historical",
+    "openelectricity_nem",
+    "orbital_chaos_nasa_ssc",
+    "pedestrian_counts",
+    "phenology_normal_hawaii",
+    "polymarket_crypto_5m_15m",
+    "port_activity",
+    "quitobench",
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+    "rideshare",
+    "salesforecastingwomartstore",
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+    "uk_electricity_generation_mix_2019_2026",
+    "uncertainty_1m",
+    "us_births-monash",
+    "us_labor",
+    "us_term_structure",
+    "vehicle_sales",
+    "vehicle_supply",
+    "vehicle_trips",
+    "water_quality_darwin",
+    "weather_data_bavaria",
+    "wids2023_competition_weather_forecasting",
+    "wind_4_seconds",
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+}

final-input-status.json

@@ -0,0 +1,24 @@
+{
+  "schema": "compressionbenchmark-current-input-status-v2",
+  "status": "known_incomplete_input_versions_quarantined_pending_replacement",
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+      "rerun_requirement": "All 85 active families on the new full114 input after independent input acceptance."
+    },
+    {
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+      "classification": "proven_old_input_incomplete_new_input_required",
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+      "rerun_requirement": "All 85 active families on a full-row target-only input after independent input acceptance; preserve all 7,018,638 source target NaNs as canonical NaN bits."
+    }
+  ],
+  "policy": "Apply the user-authorized mean numerical-sequence length >=128 rule to every dataset with admitted numeric payload. The timestamp-only medium_posts input has no admitted scientific numeric payload and is separately ineligible, not assigned a zero mean. Keep archived evidence. Exclude superseded incomplete inputs across all active language/type views until new exact accepted input/result bindings are published.",
+  "raw_science_payload_modified": false,
+  "sequence_length_audit_sha256": "9c340b8f314984881230f0bf3122f45fde64b885ce3e26baf1ec3df1f2540df4"
+}

final-scope.js

@@ -0,0 +1,26 @@
+export const FINAL160_SCOPE = {"active_algorithm_count": 85, "created_at": "2026-10-13T20:23:43.365976+08:00", "dataset_count": 137, "datasets": ["Air-Quality", "Bar-Crawl", "Bird-migration", "Comments", "ETDataset-ett", "IGlocations2", "METR-LA", "Metro-Traffic", "NYC_Yellow_Taxi_Trip", "PEMS-BAY", "RealEstate1", "RealEstate2", "Romance", "SpatialEpiBench", "Telco", "TrainsUK2", "Uberlandia", "Wins", "YaleLanguages", "ahr999-dataset", "ast_spacemobile_fleet_data", "australia_solar", "australian_electricity_demand", "auto_production_sf", "azure2019_u", "bitcoin", "bitcoin_price_timeseries", "coastal_t_s", "commodity_import", "commodity_production", "covid_deaths-monash", "cphl", "crypto", "crypto_data", "current_velocity", "deep-space-probes", "dst-index", "ecdc_covid", "eea_pm25_forecasting", "electricity_demand", "electricity_load_2011_2014", "electricity_production", "enedis-with-holidays", "epf_electricity_price", "eweld_load", "exchange_rate", "faang_engineered_time_series_features_2013_2025", "finland_traffic", "forex_factory_calendar", "fred_md", "freshretailnet_lt", "fulus_lyd_rates_2025", "geomagnetic_kp_index", "global_influenza", "global_price", "gluco_tsfm_benchmark", "goes_omni_electron_flux_forecasting", "greatplains_multisource_2000_2024", "hourly-stock-data-2023", "hourly_electricity_demand_brazil", "indian_stock_hourly_2017_2021", "job_claims", "jolts", "kaggle_web_traffic", "kdd_cup_2018", "knowable", "kraken-trading-data", "kuiper_fleet_data", "london_smart_meters", "m2oe2_timeseries", "meteoblue", "metropt_3", "multi_model_trading_data", "national_illness", "ne_china_wind", "nifty50_market_regime", "nn5_daily", "oikolab_weather", "oil_price", "omie_blockchain_study", "online_retail_2_uci", "ooni_censorship_historical", "openelectricity_nem", "orbital_chaos_nasa_ssc", "pedestrian_counts", "phenology_normal_hawaii", "polymarket_crypto_5m_15m", "port_activity", "quitobench", "rating", "reg_forecasting_v2", "rideshare", "salesforecastingwomartstore", "saugeenday-monash", "scaphandre_cpu_usage", "sg_carpark", "sg_pm25", "sg_weather", "sleep_score_fitbit", "solar_10_minutes", "solar_4_seconds", "solusdt_enhanced_180", "spx", "stock_dataset", "substorm_onsets", "sunspot", "telangana_weather", "temperature_rain", "timeseries-1m-QQQ-10y", "timeseries_1mn_sp500", "timeseries_1mn_stocks", "tourism_monthly", "tourism_monthly-zaai-ai", "tourism_residuals", "traffic", "traffic_hourly", "trxusdt_spot_1m_05_2021_to_03_2026", "uk_electricity_generation_mix_2019_2026", "uncertainty_1m", "us_births-monash", "us_labor", "us_term_structure", "vehicle_sales", "vehicle_supply", "vehicle_trips", "water_quality_darwin", "weather_data_bavaria", "wids2023_competition_weather_forecasting", "wind_4_seconds", "wind_farms_minutely", "wordle_mcm_dataset", "wui_global", "xauusd", "xauusd_1m", "xinyi_public_gym", "xrpusdt_spot_1m_05_2021_to_03_2026", "yongchun_public_gym"], "excluded_dataset_count": 23, 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"/Users/xiaojinzhao/Documents/GitHub/web_compression/analysis_outputs/six_dataset_numeric_repair_20261012_v1/yahoo_github/output_v2/github_top_projects/series_map.csv", "sha256": "a15818feb0a413c06652f6e68ef88dd97edd19e603b726bc2ba7f102ac3232d9"}}, "valid_value_count": 234954}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "globalstar_fleet_data", "input_version": "published_baseline_typed_input", "mean_valid_values_per_sequence": 109.25, "nonempty_numeric_sequence_count": 20, "source_bindings": {"block_catalog_bytes": 44300, "block_catalog_path": "/data/root/web_compression/datasets164/globalstar_fleet_data/typed/full/blocks.jsonl", "block_catalog_sha256": "c8378f97eaaafb2678dc119ed5fc922077f6e96324059dcc25515c01137ebafd", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/globalstar_fleet_data/typed/dataset_manifest.json", "typed_manifest_sha256": 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"7546af1d7f49ee5480d791cb942351a4a50ac6ac644dd770a1374afd6aa42cbf"}, "valid_value_count": 85088}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "tourism_yearly", "input_version": "published_baseline_typed_input", "mean_valid_values_per_sequence": 24.627413127413128, "nonempty_numeric_sequence_count": 1036, "source_bindings": {"block_catalog_bytes": 2271148, "block_catalog_path": "/data/root/web_compression/datasets164/tourism_yearly/typed/full/blocks.jsonl", "block_catalog_sha256": "c20cdff15822ba6809d02fcfb1e0d3cffc9bb5d6eb3b05c618cf1f7f3a7a76a4", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/tourism_yearly/typed/dataset_manifest.json", "typed_manifest_sha256": "8a9f17a8b9437b4c186ddb3a8355c76f330ed2c451385cf00d4364fd9199d55d"}, "valid_value_count": 25514}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "traffic_weekly", "input_version": "published_baseline_typed_input", "mean_valid_values_per_sequence": 104.0, "nonempty_numeric_sequence_count": 1724, "source_bindings": {"block_catalog_bytes": 3790280, "block_catalog_path": "/data/root/web_compression/datasets164/traffic_weekly/typed/full/blocks.jsonl", "block_catalog_sha256": "4aa6a797240415e2b47b9ced3ecb64d31d643c5f72bb2d97fc92500b02f63e54", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/traffic_weekly/typed/dataset_manifest.json", "typed_manifest_sha256": "9ba985cbc9ca1ead89e8c0ee0a533bd8517c596d0a872b467b3990f73a281440"}, "valid_value_count": 179296}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "traqq_weekly_activity", "input_version": "published_correct_source_semantics_unchanged_typed_input", "mean_valid_values_per_sequence": 4.895522388059701, "nonempty_numeric_sequence_count": 335, "source_bindings": {"block_catalog_bytes": 740698, "block_catalog_path": "/data/root/web_compression/datasets164/traqq_weekly_activity/typed/full/blocks.jsonl", "block_catalog_sha256": "2e35d39cbe95790c6473a49f272792e2828f623533117b12f91d9b1b5c223b61", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/traqq_weekly_activity/typed/dataset_manifest.json", "typed_manifest_sha256": "7e9b85464124cdecd8b19b0a7c6fe59632b6856e8dd3ea340add0fb9794ba1cd"}, "valid_value_count": 1640}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "trending_stocks_yahoo_finance", "input_version": "corrected_accepted_input", "mean_valid_values_per_sequence": 5.7372982158028885, "nonempty_numeric_sequence_count": 17655, "source_bindings": {"columns_csv": {"bytes": 1227, "path": "/Users/xiaojinzhao/Documents/GitHub/web_compression/analysis_outputs/six_dataset_numeric_repair_20261012_v1/yahoo_github/output_v2/trending_stocks_yahoo_finance/columns.csv", "sha256": "df6236a75d3eb6f67efe9dcda7d1186b06d5d915cd7c70ea65d06750c1ca2489"}, "data_csv": {"bytes": 1073819, "path": "/Users/xiaojinzhao/Documents/GitHub/web_compression/analysis_outputs/six_dataset_numeric_repair_20261012_v1/yahoo_github/output_v2/trending_stocks_yahoo_finance/data.csv", "sha256": "fd8de8b6166d6875f5bb961d423e34bb438460625c884a5c2ddc6784efea4637"}, "dataset_manifest": {"bytes": 1507, "path": "/Users/xiaojinzhao/Documents/GitHub/web_compression/analysis_outputs/six_dataset_numeric_repair_20261012_v1/yahoo_github/output_v2/trending_stocks_yahoo_finance/dataset_manifest.json", "sha256": "79349da2c5a892dc988104b7ea56ee2bbc5a7128919c2caee7228107d1fc0eb6"}, "method": "local_exact_corrected_wide_csv_scan", "series_map_csv": {"bytes": 252602, "path": "/Users/xiaojinzhao/Documents/GitHub/web_compression/analysis_outputs/six_dataset_numeric_repair_20261012_v1/yahoo_github/output_v2/trending_stocks_yahoo_finance/series_map.csv", "sha256": "e29ecee1ceaf947b33be5c1d143eb12e17c540f47cea39a88a2fba63423f4ee4"}}, "valid_value_count": 101292}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "us_stock_financial_ratios", "input_version": "published_correct_source_semantics_unchanged_typed_input", "mean_valid_values_per_sequence": 9.896346929627969, "nonempty_numeric_sequence_count": 8924, "source_bindings": {"block_catalog_bytes": 20865599, "block_catalog_path": "/data/root/web_compression/datasets164/us_stock_financial_ratios/typed/full/blocks.jsonl", "block_catalog_sha256": "04e12b341f09d4aae337077377cd4c17e7922a8a259a5465699385f101add238", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/us_stock_financial_ratios/typed/dataset_manifest.json", "typed_manifest_sha256": "8d2625899f1ce97ae2a1d9c4c2b82132948a31bd7803bca2818d250483812c04"}, "valid_value_count": 88315}, {"classification": "exclude_mean_valid_values_per_sequence_below_128", "dataset_id": "zillow_viewer", "input_version": "published_baseline_typed_input", "mean_valid_values_per_sequence": 32.28351815443223, "nonempty_numeric_sequence_count": 244651, "source_bindings": {"block_catalog_bytes": 640656958, "block_catalog_path": "/data/root/web_compression/datasets164/zillow_viewer/typed/full/blocks.jsonl", "block_catalog_sha256": "0e78b5cb919cbf544e49d916fe8d72ccc1b6b61ab93c635f115d149111ea33c4", "host": "192", "mirror_acceptance": "dataset164-final-acceptance-v2", "typed_manifest_path": "/data/root/web_compression/datasets164/zillow_viewer/typed/dataset_manifest.json", "typed_manifest_sha256": "1f60337db1ce69b7562ce2d20908e7158455f36481d4bb8cc3ffc130be997494"}, "valid_value_count": 7898195}], "expected_active_pair_count": 11645, "input_version_policy": "current corrected input where available; published accepted baseline otherwise", "schema": "compressionbenchmark-min128-dataset-scope-v1", "scope_id": "min128_20261013_v1", "selection_rule": "retain admitted numeric payload datasets with mean valid values per complete scientific sequence >=128", "sequence_length_audit_sha256": "9c340b8f314984881230f0bf3122f45fde64b885ce3e26baf1ec3df1f2540df4", "source_final160_scope_sha256": "92a995e4278971077fe60dabd913e26e1afdccaeca1ec6181ae1de3f50929889", "source_min32_sequence_length_audit_sha256": "cdce9b3f988f48639164e850ac16a345bbb23066cbd60f2c8989c21adfcd9365", "status": "frozen_user_authorized_min128_test_scope"};
+export const FINAL160_INPUT_STATUS = {"schema": "compressionbenchmark-current-input-status-v2", "status": "known_incomplete_input_versions_quarantined_pending_replacement", "scope_sha256": "b56f9bd610e0bf63ab50d719ce70cf92ddc1b79e6b30fd2c6e2f707944b2ba72", "format_audit_sha256": "2d3fb152f4f040a044a49fb23a0a34eb217ad0a54668f1fa0c65281d72064ba8", "scope_id": "min128_20261013_v1", "pending_datasets": [{"dataset_id": "freshretailnet_lt", "classification": "proven_old_input_incomplete_new_input_required", "reason": "The old export retained 66/114 table-qualified numeric columns and dropped 48 trailing train columns under the 1 GB cap. All 7,939,549 source rows were retained, but the value-column universe is incomplete.", "rerun_requirement": "All 85 active families on the new full114 input after independent input acceptance."}, {"dataset_id": "kaggle_web_traffic", "classification": "proven_old_input_incomplete_new_input_required", "reason": "The old export retained only 33,860,956 of 116,476,879 eligible source rows because of the 1 GB cap (82,615,923 rows lost). The one scientific value is FLOAT target; physical Time is the temporal coordinate and TAG fields (including start_timestamp) are metadata, not value payload.", "rerun_requirement": "All 85 active families on a full-row target-only input after independent input acceptance; preserve all 7,018,638 source target NaNs as canonical NaN bits."}], "policy": "Apply the user-authorized mean numerical-sequence length >=128 rule to every dataset with admitted numeric payload. The timestamp-only medium_posts input has no admitted scientific numeric payload and is separately ineligible, not assigned a zero mean. Keep archived evidence. Exclude superseded incomplete inputs across all active language/type views until new exact accepted input/result bindings are published.", "raw_science_payload_modified": false, "sequence_length_audit_sha256": "9c340b8f314984881230f0bf3122f45fde64b885ce3e26baf1ec3df1f2540df4"};
+export const ACTIVE_CPP_FAMILIES = ["2DIFF", "ALP", "ANS", "Arithmetic", "BOS", "BSC", "BUFF", "Blosc", "Blosc2", "BriefLZ", "Brotli", "Bzip2", "Bzip3", "CORAD", "CSC", "Camel", "Chimp", "Chimp128", "Crush", "DBP", "Density", "Dict.", "Elf", "Elf+", "FPC", "FPCmp-R", "FPCmp-S", "FPZIP", "FSE", "FastLZ", "FastLZMA2", "FastPFor", "Gipfeli", "Gorilla", "Huff0", "Huffman", "Kanzi", "LECO", "LZ4", "LZ4 Fast", "LZ4HC", "LZAV", "LZF", "LZFSE", "LZG", "LZHAM", "LZJB", "LZMAT", "LZO1", "LZO2A", "LZRW", "LZSSE", "LZVN", "Libdeflate", "Lizard", "Lzlib", "NRV", "PFPC", "PPMD8", "Pithy", "Pongo", "QuickLZ", "REGER", "RLBE", "RLE", "SLZ", "SPDP", "SVB", "Shrinker", "Simple8b", "Snappy", "Sprintz", "TAMP", "Tornado", "WFLZ", "XPACK", "XZ", "YALZ77", "Yappy", "ZXC", "Zlib", "Zlib-ng", "Zling", "Zstd", "gzip"];
+const EXPECTED_SCOPE_SHA256="b56f9bd610e0bf63ab50d719ce70cf92ddc1b79e6b30fd2c6e2f707944b2ba72";
+
+export function applyFinal160Scope(snapshot,scope=FINAL160_SCOPE,status=FINAL160_INPUT_STATUS) {
+  if(scope?.schema!=='compressionbenchmark-min128-dataset-scope-v1'||scope?.status!=='frozen_user_authorized_min128_test_scope'||scope.scope_id!==FINAL160_SCOPE.scope_id)throw new Error('Invalid numerical-sequence scope contract.');
+  if(!Array.isArray(scope.datasets)||scope.datasets.length!==FINAL160_SCOPE.dataset_count||new Set(scope.datasets).size!==scope.datasets.length||scope.datasets.some((id,index)=>id!==FINAL160_SCOPE.datasets[index]))throw new Error('Dataset identities differ from the frozen scope.');
+  if(status?.schema!=='compressionbenchmark-current-input-status-v2'||status.scope_id!==scope.scope_id||status.scope_sha256!==EXPECTED_SCOPE_SHA256||!Array.isArray(status.pending_datasets))throw new Error('Invalid current-input gate.');
+  const ids=new Set(scope.datasets),blocked=new Set(status.pending_datasets.map(item=>item.dataset_id)),families=new Set(ACTIVE_CPP_FAMILIES);
+  if(blocked.size!==status.pending_datasets.length||[...blocked].some(id=>!ids.has(id)))throw new Error('Invalid pending input identity.');
+  const known=new Set((snapshot.datasets||[]).map(item=>item.key??item.dataset));
+  if([...ids].some(id=>!known.has(id)))throw new Error('A selected identity is absent from the immutable source snapshot.');
+  if(!Array.isArray(snapshot.dataset_results))throw new Error('Expected the receipt-bound decoded result view.');
+  const selected=snapshot.dataset_results.filter(row=>ids.has(row.dataset)&&!blocked.has(row.dataset)&&(row.comparison_profile!=='cpp'||families.has(row.family)));
+  return {...snapshot,datasets:snapshot.datasets.filter(item=>ids.has(item.key??item.dataset)),dataset_results:selected,
+    active_dataset_scope:{scope_id:status.scope_id,dataset_count:ids.size,pending_dataset_ids:[...blocked].sort(),historical_archive_dataset_count:known.size,retained_result_rows:selected.length}};
+}
+export function filterFinal160Catalog(catalog) {
+  const ids=new Set(FINAL160_SCOPE.datasets);
+  return {...catalog,datasets:(catalog.datasets||[]).filter(item=>ids.has(item.key??item.dataset))};
+}
+export function final160CoverageForFamily(ledger,family) {
+  const ids=new Set(FINAL160_SCOPE.datasets),pending=new Set(FINAL160_INPUT_STATUS.pending_datasets.map(item=>item.dataset_id));
+  return ledger.filter(item=>item.canonical_family===family&&ids.has(item.dataset)).map(item=>({dataset:item.dataset,status:pending.has(item.dataset)?'replacement_input_pending':item.status,scopes:pending.has(item.dataset)?[]:(item.selected_scopes||[]),reason:pending.has(item.dataset)?'Old incomplete input excluded; corrected-input experiment pending.':item.reason}));
+}

index.html

@@ -7,7 +7,7 @@
   <meta name="color-scheme" content="light dark">
   <title>THULab | Time Series Compression Benchmark</title>
   <link rel="stylesheet" href="style.css?v=20261013-cpp-latest-v1">
-  <script src="app.js?v=20261013-cpp-latest-v7" type="module"></script>
+  <script src="app.js?v=20261013-min128-input-gate-v1" type="module"></script>
 </head>
 <body>
   <a class="skip-link" href="#content">Skip to results</a>
@@ -16,7 +16,7 @@
     <div class="header-links"><a href="https://github.com/xjz17/web_compression" target="_blank" rel="noopener">Project code ↗</a><button id="theme" class="quiet-button" type="button" aria-label="Switch color theme">Theme</button></div>
   </header>
   <main id="content">
-<section class="snapshot-notice" aria-label="Current C++ result selection"><strong>C++ · latest receipt-bound result per dataset and method</strong><p>The C++ view combines published campaigns and selects the newest receipt-bound authority for each logical dataset/method pair. Corrected inputs for five datasets are included; terminal N/A or unsupported conclusions block fallback to older numbers. Prior versions remain preserved in the downloadable snapshot and evidence. FreshRetailNet-50K full114 is still pending and is not presented as a newer result.</p><a href="?profile=cpp#overall">View combined C++ results</a></section>
+<section class="snapshot-notice" aria-label="Current dataset scope and input status"><strong>Test scope: 137 datasets · numerical sequences ≥128 values on average</strong><p>Applied uniformly to the previous 160 dataset identities, independent of compression rate. Timestamp-only medium_posts is separately ineligible. 2 old incomplete inputs remain excluded from active rankings pending corrected-input results. Historical datasets and results are preserved.</p><a href="final-dataset-scope.json" download>Dataset scope</a> · <a href="sequence-length-audit.json" download>Sequence-length audit</a> · <a href="final-input-status.json" download>Input and retest status</a></section>
 
     <section class="intro" aria-labelledby="title">
       <p class="eyebrow">Time-series systems research</p>
@@ -101,7 +101,7 @@
         <label class="documentation-search">Search operators<input id="operator-doc-search" type="search" placeholder="Family, A-ID, implementation, I/O, or reference" autocomplete="off"></label>
       </div>
       <section id="operator-effect-results" class="operator-effect-results" aria-labelledby="operator-effect-title">
-        <div class="operator-effect-head"><div><h3 id="operator-effect-title">Verified matched-omission byte effects</h3><p class="muted">Byte-only measurements from the independently accepted full union. Every admitted row is lossless and passed fresh baseline and omission round-trip checks against the same original-byte denominator. Ratios above 1 mean omission increased reconstructable bytes. Datasets receive equal weight; timing is unavailable.</p></div><a class="quiet-button" href="operator-byte-effect-full-union.csv.gz" download>Download 41,090-row evidence</a></div>
+        <div class="operator-effect-head"><div><h3 id="operator-effect-title">Historical matched-omission byte effects</h3><p class="muted">Historical byte-only measurements from the independently accepted archived union. Its dataset identities and some inputs predate the current sequence-length filter and numeric corrections; these values are historical, not current-scope/current-input results. Every admitted row is lossless and passed fresh baseline and omission round-trip checks against the same original-byte denominator. Ratios above 1 mean omission increased reconstructable bytes. Datasets receive equal weight; timing is unavailable.</p></div><a class="quiet-button" href="operator-byte-effect-full-union.csv.gz" download>Download 41,090-row evidence</a></div>
         <p id="operator-effect-status" class="documentation-status" aria-live="polite">Loading verified byte effects…</p>
         <div class="table-wrap operator-effect-table-wrap"><table id="operator-effect-table"><thead><tr><th>Omitted operator</th><th>Omission / baseline</th><th>Byte change</th><th>Baseline / original</th><th>Omission / original</th><th>Dataset support</th><th>Rows</th></tr></thead><tbody><tr><td colspan="7" class="loading-cell">Loading verified byte effects…</td></tr></tbody></table></div>
         <p class="reference-source">These matched diagnostic recipes measure byte effects only. They do not provide timing rankings or universal causal attribution for every catalog method.</p>
@@ -130,7 +130,7 @@
       <p>By default, each family selects the lowest-rate exact variant independently for each dataset only when all candidates for that dataset/family have one exact support set. This view can choose different binaries across datasets. Ambiguous support sets are omitted. Switch Method rows to Exact configuration to inspect fixed variants, source profiles, binaries, and support hashes.</p>
       <p>These are published measurements, not experiments executed inside this Space. A reported ratio alone does not establish lossless reconstruction; round-trip checks and hardware details are shown only when their evidence exists in the source snapshot. Timings without an exact admitted source/build/binary/boundary chain are unavailable to timing and composite rankings.</p>
       <h3>Download and provenance</h3>
-      <p><a href="data.json" download>Result snapshot (JSON)</a> · <a href="manifest.json" download>Source manifest (JSON)</a> · <a href="native86-coverage.json" download>Native86 coverage summary (JSON)</a> · <a href="native86-terminal-matrix.jsonl.gz" download>13,940-pair terminal matrix (JSONL.GZ)</a> · <a href="native86-v9-current154-thin.jsonl.gz" download>Current 154-pair V9 evidence (JSONL.GZ)</a> · <a href="native86-recovery-reconciliation.json" download>Recovery reconciliation (JSON)</a></p>
+      <p><a href="data.json" download>Result snapshot (JSON)</a> · <a href="manifest.json" download>Source manifest (JSON)</a> · <a href="native86-coverage.json" download>Native86 coverage summary (JSON)</a> · <a href="native86-terminal-matrix.jsonl.gz" download>Historical 164-dataset terminal matrix (JSONL.GZ)</a> · <a href="native86-v9-current154-thin.jsonl.gz" download>Current 154-pair V9 evidence (JSONL.GZ)</a> · <a href="native86-recovery-reconciliation.json" download>Recovery reconciliation (JSON)</a></p>
       <div id="provenance"></div>
       <h3>Contribute a result</h3>
       <p>Run the project benchmark and provide the codec/configuration, input identity, compression and decompression measurements, execution environment, and round-trip validation evidence. Results can be updated from the source project using the snapshot exporter included in the Space repository.</p>