Align paper core benchmark protocol

README.md

@@ -39,13 +39,14 @@ Public Hugging Face links:
 
 ```text
 data/paper_tables/
-  Exact CSV versions of the camera-ready main and appendix tables.
+  Exact CSV versions of the camera-ready main and appendix tables, plus the
+  raw 6-scenario x 50-draw six-family leaderboard used to regenerate Table 2.
 
 data/paper_figures/
   Paper figure assets used by the leaderboard Space for visual alignment checks.
 
 data/synthetic_full22_extension/
-  Paper-aligned 6-scenario synthetic benchmark extension with a 22-method roster.
+  Benchmark-only 6-scenario synthetic extension with a 22-method roster.
 
 data/real_proxy22/
   Real-data companion track with canonicalized public time series and proxy diagnostics.
@@ -74,13 +75,14 @@ metadata/
 
 The primary paper studies standalone decomposition as component recovery under controlled synthetic mechanisms. The main leaderboard and paper tables should be read as diagnostic capability profiles, not as a universal single-score ranking.
 
-The 22-method files are included as an extension track. Several rows correspond to decomposition-inspired neural blocks or benchmark-side mechanism proxies, not full end-to-end reproductions of the original forecasting models. These rows are useful for future benchmark growth but should not replace the primary standalone-decomposition interpretation.
+The 22-method files are included as benchmark-only expansion and transfer tracks. Several rows correspond to additional method prototypes or benchmark-side mechanism proxies, not camera-ready paper claims. These rows use the same 6-scenario, 50-draw synthetic protocol where applicable, but they should not replace the primary six-family Table 2 / Figure 3 interpretation.
 
 ## Versioned Evidence Layout
 
-The release separates the paper-aligned artifact from later companion evidence:
+The release separates the frozen camera-ready paper snapshot from benchmark extensions:
 
-- Paper-aligned version: `data/paper_tables/`, `data/synthetic_full22_extension/`, `data/real_proxy22/`, and `data/semisynth_transfer/`.
+- Camera-ready paper snapshot: `data/paper_tables/` and `data/paper_figures/`.
+- Living benchmark extensions: `data/synthetic_full22_extension/`, `data/real_proxy22/`, and `data/semisynth_transfer/`.
 - Post-rebuttal second expansion: `data/post_rebuttal_second_expansion/`.
 
 The second expansion includes the real-data additions used in the rebuttal: mechanism-aware checks on CO2 and tides, plus a broader six-dataset proxy and stability panel. It is included for transparency and follow-up analysis, not to replace the camera-ready paper tables.
@@ -108,10 +110,15 @@ reports the fraction of successful runs with valid metrics.
 
 The camera-ready Table 2 view intentionally displays only two of the five core
 metrics: Trend R2 and Seasonal spectral correlation, each split over stationary
-regimes (Tiers 1-2) and non-stationary regimes (Tier 3). Figure 3 should be read
-as the five-metric capability profile. The expanded 22-method files may include
-mean-rank convenience columns, but those are extension summaries and are not the
-primary paper definition.
+regimes (Tiers 1-2) and non-stationary regimes (Tier 3). Its displayed values
+use a tier-balanced aggregation: first compute method means separately for Tier
+1, Tier 2, and Tier 3 over valid metric values; stationary columns are the
+equal-weight average of Tier 1 and Tier 2 means, while non-stationary columns
+are Tier 3 means. Seasonal metrics are undefined for the trend-only scenario,
+so the by-tier file records metric-specific valid-row counts. Figure 3 should
+be read as the five-metric capability profile. The expanded 22-method files may
+include mean-rank convenience columns, but those are extension summaries and
+are not the primary paper definition.
 
 Machine-readable metric definitions are in:
 
@@ -123,11 +130,15 @@ site_data/v1.0.0/evaluation_metrics.json
 
 ### `paper_tables`
 
-Small CSV files matching the camera-ready manuscript tables. These are the safest files to cite directly when checking paper consistency.
+Small CSV files matching the camera-ready manuscript tables. These are the safest files to cite directly when checking paper consistency. The paper-core reproduction files are:
+
+- `global_performance_summary.csv`: the rounded Table 2 values used by the manuscript and leaderboard.
+- `paper_core_50draw_leaderboard.csv`: raw 1,800-row paper-core output (6 scenarios x 50 draws x 6 methods).
+- `paper_core_50draw_by_tier.csv`: tier-level means and metric-specific valid-row counts used to derive the tier-balanced Table 2 columns.
 
 ### `synthetic_full22_extension`
 
-This track uses the paper-aligned 6-scenario synthetic protocol with length 512, 50 draws per scenario, true-period-given evaluation, and a 22-method roster. It includes raw rows, overall summaries, by-scenario summaries, by-tier summaries, coverage, protocol matrix, and backend-selection metadata.
+This benchmark-only track uses the same 6-scenario synthetic generator and five-metric evaluation protocol with length 512, 50 draws per scenario, true-period-given evaluation, and a 22-method roster. It includes raw rows, overall summaries, by-scenario summaries, by-tier summaries, coverage, protocol matrix, and backend-selection metadata. It is an expanded roster view, while the camera-ready Table 2 / Figure 3 source is the six-family paper table under `data/paper_tables/`.
 
 ### `real_proxy22`
 
@@ -135,7 +146,7 @@ This track uses public real time series with known periods, known mechanisms, or
 
 ### `semisynth_transfer`
 
-This track injects known mechanisms into real monthly backgrounds. Large downloaded source files are intentionally excluded from this release; the canonical small CSV backgrounds and metric tables are included.
+This benchmark-only transfer track injects known mechanisms into six real monthly backgrounds using a 22-method roster, three mechanisms, two background scales, and eight windows per setting. The released five-metric ranking is `data/semisynth_transfer/results/summary/ranking_paper_5metric_overall.csv`. It is a living benchmark extension rather than a camera-ready paper table. Large downloaded source files are intentionally excluded from this release; the canonical small CSV backgrounds and metric tables are included.
 
 ### `post_rebuttal_second_expansion`
 
@@ -181,10 +192,15 @@ $env:PYTHONPATH = "$PWD\src"
 python scripts/run_paper_benchmark.py
 ```
 
-This command runs the paper-aligned core synthetic benchmark: 6 scenarios,
-50 draws per scenario, and therefore 300 generated synthetic series. Because
-each generated series is evaluated by each selected decomposition method, the
-raw `leaderboard.csv` has one row per scenario, draw, seed, and method.
+This command runs the camera-ready core synthetic benchmark: 6 scenarios,
+50 deterministic draws per scenario, and therefore 300 generated synthetic
+series. The default method set is the six-family Table 2 roster
+(`ma_baseline,stl,ssa,emd,vmd,wavelet`). Because each generated series is
+evaluated by each selected decomposition method, the raw `leaderboard.csv` has
+one row per scenario, draw, seed, and method.
+The public `data/paper_tables/paper_core_50draw_leaderboard.csv` file is this
+raw output after stable row sorting; `paper_core_50draw_by_tier.csv` and
+`global_performance_summary.csv` are deterministic aggregations of it.
 The script pins imports to the bundled source snapshot, so local editable
 installs of other `tsdecomp` versions will not change the run.
 
@@ -197,7 +213,7 @@ python scripts/run_paper_benchmark.py --smoke
 Equivalent direct CLI call for the full paper core run:
 
 ```bash
-python -m tsdecomp run_leaderboard --suite core --methods core --seeds 0 --n_samples 50 --length 512 --dt 1.0 --out artifacts/paper_core_benchmark --aggregate
+python -m tsdecomp run_leaderboard --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet --seeds 0 --n_samples 50 --length 512 --dt 1.0 --out artifacts/paper_core_benchmark --aggregate
 ```
 
 The script entrypoint is recommended for reproduction; the direct module CLI is

code/TSDecompose/README.md

@@ -44,9 +44,9 @@ python scripts/run_paper_benchmark.py
 The default command runs the paper-aligned core synthetic benchmark:
 
 - 6 scenarios;
-- 50 generated draws per scenario;
+- 50 deterministic generated draws per scenario;
 - 300 generated synthetic series total;
-- the source-code `core` method preset (`stl`, `mstl`, `ssa`, `emd`, `ceemdan`, `vmd`, `wavelet`).
+- the camera-ready six-family Table 2 roster (`ma_baseline`, `stl`, `ssa`, `emd`, `vmd`, `wavelet`).
 
 The raw result table is written to:
 
@@ -54,6 +54,15 @@ The raw result table is written to:
 artifacts/paper_core_benchmark/leaderboard.csv
 ```
 
+In the dataset release, this same paper-core raw output is stored as
+`data/paper_tables/paper_core_50draw_leaderboard.csv` after stable row sorting.
+The manuscript Table 2 values are derived from
+`data/paper_tables/paper_core_50draw_by_tier.csv` using the tier-balanced rule:
+stationary columns average Tier 1 and Tier 2 means with equal weight, while
+non-stationary columns use Tier 3 means. The by-tier file records
+metric-specific valid-row counts because seasonal metrics are undefined for the
+trend-only scenario.
+
 Aggregated summaries are written under:
 
 ```text
@@ -72,13 +81,13 @@ and the smaller `stl,wavelet` method subset.
 ## Optional Direct CLI
 
 ```bash
-python -m tsdecomp validate --suite core --methods core
+python -m tsdecomp validate --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet
 ```
 
 Windows PowerShell:
 
 ```powershell
-python -m tsdecomp validate --suite core --methods core
+python -m tsdecomp validate --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet
 ```
 
 Use the script entrypoint above for reproduction. The direct module CLI is

code/TSDecompose/scripts/run_paper_benchmark.py

@@ -3,6 +3,7 @@
 
 Default run:
   6 scenarios x 50 generated draws = 300 synthetic series.
+  Each series is evaluated by the six camera-ready Table 2 method families.
 
 The row count in leaderboard.csv is larger because each generated series is
 evaluated by every requested method.
@@ -53,6 +54,7 @@ from tsdecomp.leaderboard import run_leaderboard  # noqa: E402
 
 
 PAPER_SUITE = "core"
+PAPER_METHODS = "ma_baseline,stl,ssa,emd,vmd,wavelet"
 PAPER_SEEDS = "0"
 PAPER_N_SAMPLES = 50
 PAPER_LENGTH = 512
@@ -81,8 +83,9 @@ def build_parser() -> argparse.ArgumentParser:
         "--methods",
         default=None,
         help=(
-            "Comma-separated method list or preset. Defaults to 'core' for the "
-            "full paper run and 'stl,wavelet' for --smoke."
+            "Comma-separated method list or preset. Defaults to the "
+            "camera-ready six-method Table 2 set for the full paper run, "
+            "or 'stl,wavelet' for --smoke."
         ),
     )
     parser.add_argument(
@@ -135,7 +138,7 @@ def main() -> None:
 
     methods = args.methods
     if methods is None:
-        methods = SMOKE_METHODS if args.smoke else "core"
+        methods = SMOKE_METHODS if args.smoke else PAPER_METHODS
 
     n_samples = args.n_samples
     if n_samples is None:

data/paper_tables/global_performance_summary.csv

@@ -1,7 +1,7 @@
-method_family,stationary_trend_r2,stationary_seasonal_spectral_corr,nonstationary_trend_r2,nonstationary_seasonal_spectral_corr
-Smoothing (MA),0.586,0.921,-0.913,0.792
-LOESS (STL),0.967,0.973,0.105,0.973
-Subspace (SSA),0.941,0.825,0.104,0.988
-Sifting (EMD),0.947,0.819,-0.041,0.994
-Spectral (VMD),0.043,0.260,-1.999,0.151
-Wavelet,0.829,0.942,-0.388,0.982
+method_family,stationary_trend_r2,stationary_seasonal_spectral_corr,nonstationary_trend_r2,nonstationary_seasonal_spectral_corr
+Smoothing (MA),0.571,0.938,-0.607,0.779
+LOESS (STL),0.965,0.98,0.247,0.973
+Subspace (SSA),0.929,0.876,0.195,0.987
+Sifting (EMD),0.936,0.867,0.119,0.964
+Spectral (VMD),0.077,0.185,-1.488,0.046
+Wavelet,0.838,0.94,-0.183,0.983

data/paper_tables/paper_core_50draw_by_tier.csv

@@ -0,0 +1,19 @@
+scenario_tier,method,status_ok_rows,total_rows,metric_T_r2_mean,metric_T_r2_valid_rows,metric_T_dtw_mean,metric_T_dtw_valid_rows,metric_S_spectral_corr_mean,metric_S_spectral_corr_valid_rows,metric_S_maxlag_corr_mean,metric_S_maxlag_corr_valid_rows,metric_S_r2_mean,metric_S_r2_valid_rows
+1,emd,100,100,0.9632752190908691,100,0.860219462817183,100,0.9917559229031864,50,0.9457818870902791,50,0.8854553730472193,50
+1,ma_baseline,100,100,0.3755548966376155,100,5.500637053509605,100,0.9980462385314515,50,0.8983656758466055,50,0.5682721381292004,50
+1,ssa,100,100,0.9724700039460159,100,0.5825512343385894,100,0.9999613398074225,50,0.9920490531316182,50,0.9830115881923378,50
+1,stl,100,100,0.9915121877131883,100,0.29555699366017907,100,0.9998725842629397,50,0.9517473461604393,50,0.8958359274445438,50
+1,vmd,100,100,-0.4905175861023846,100,7.697509691574182,100,-0.050289501708312756,50,0.0021892512162106316,50,-0.04487469736077882,50
+1,wavelet,100,100,0.8004863646830966,100,2.409388569760423,100,0.9912837014117635,50,0.9207078638454429,50,0.8463959620747508,50
+2,emd,100,100,0.9092488272900404,100,2.6246327667857265,100,0.742383503436914,100,0.6668923513470455,100,0.44140523776841306,100
+2,ma_baseline,100,100,0.7666924685906201,100,4.082477882327422,100,0.8789150756983019,100,0.8777907233082455,100,0.7657596157688267,100
+2,ssa,100,100,0.8860241208071092,100,2.402275362107129,100,0.7514603777538018,100,0.7535636092482233,100,0.5721562142787083,100
+2,stl,100,100,0.9383149494871883,100,1.871204892573259,100,0.9600792873094613,100,0.8580340579608056,100,0.7181025032925986,100
+2,vmd,100,100,0.6435788317542215,100,4.528224917986381,100,0.41982638378399423,100,0.4152585282927285,100,0.31206278691074735,100
+2,wavelet,100,100,0.8763668865963092,100,2.964544855625089,100,0.8877966589852118,100,0.8043219303771838,100,0.5987607573037969,100
+3,emd,100,100,0.11921738267153768,100,6.0822885067587045,100,0.964087190987869,100,0.8433100034565808,100,0.6914733515142011,100
+3,ma_baseline,100,100,-0.6071629001999631,100,9.073156086389606,100,0.7785481310287296,100,0.3710597699305178,100,0.16142942374271005,100
+3,ssa,100,100,0.195188894033973,100,5.53868721179627,100,0.9873651474904723,100,0.9235433338703318,100,0.8403859477815182,100
+3,stl,100,100,0.24702595798520258,100,4.384936412336581,100,0.9727162870723237,100,0.7860316964733616,100,0.5943059730312088,100
+3,vmd,100,100,-1.4877187612474683,100,12.458440192851697,100,0.0462177466385445,100,0.09532723064208445,100,0.026185809113734075,100
+3,wavelet,100,100,-0.18330960408955083,100,6.919057730577578,100,0.98292207775555,100,0.8627857378229012,100,0.7297481719505625,100

data/semisynth_transfer/README.md

@@ -1,6 +1,6 @@
 # Semi-Synthetic Transfer Track
 
-This directory contains the semi-synthetic transfer checks used to qualify method-level claims under real low-frequency backgrounds.
+This directory contains benchmark-only 22-method semi-synthetic transfer checks under real low-frequency backgrounds. These files support the living benchmark and are not camera-ready paper tables.
 
 Large downloaded source files are excluded from this Hugging Face release. The included files are:
 
@@ -8,4 +8,6 @@ Large downloaded source files are excluded from this Hugging Face release. The i
 - method defaults and benchmark manifest under `configs/`;
 - merged and summarized metric outputs under `results/`.
 
-The headline interpretation is that the mechanism-level picture transfers, while method-level conclusions become more conservative. In particular, CEEMDAN can preserve spectral agreement while losing amplitude/shape fidelity because of mode mixing under real backgrounds.
+The extension ranking source is `results/summary/ranking_paper_5metric_overall.csv`, which ranks methods by mean within-setting rank over the five paper recovery metrics.
+
+The headline interpretation is that the mechanism-level picture transfers, while method-level conclusions become more conservative. In the aligned 22-method track, STL ranks first, followed by AMD block, MA baseline, Times2D block, and a tie among Autoformer, DLinear, and the moving-average decomposition block; CEEMDAN ranks sixth. CEEMDAN preserves strong seasonal agreement under real backgrounds, but its trend recovery remains sensitive to mode mixing against low-frequency real structure.

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metadata/dataset_schema.json

@@ -4,6 +4,10 @@
     "paper_tables": {
       "path": "data/paper_tables",
       "description": "Exact CSV versions of the camera-ready main-text and appendix tables.",
+      "paper_core_raw": "data/paper_tables/paper_core_50draw_leaderboard.csv",
+      "paper_core_by_tier": "data/paper_tables/paper_core_50draw_by_tier.csv",
+      "table2_display": "data/paper_tables/global_performance_summary.csv",
+      "table2_aggregation": "Tier-balanced over valid metric values: stationary columns are the equal-weight average of Tier 1 and Tier 2 means; non-stationary columns are Tier 3 means. Seasonal metrics are undefined for the trend-only scenario, so metric-specific valid-row counts are provided.",
       "recommended_for_citation_checks": true
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@@ -13,7 +17,7 @@
     "synthetic_full22_extension": {
       "raw": "data/synthetic_full22_extension/results/merged/leaderboard_full22_raw.csv",
       "summary_dir": "data/synthetic_full22_extension/results/summary",
-      "description": "Paper-aligned 6-scenario synthetic extension with a 22-method roster."
+      "description": "Benchmark-only 6-scenario, 50-draw synthetic extension with a 22-method roster."
     },
     "real_proxy22": {
       "raw": "data/real_proxy22/results/summary/latest_round_raw_proxy_metrics.csv",
@@ -23,7 +27,7 @@
     "semisynth_transfer": {
       "raw": "data/semisynth_transfer/results/merged/semisynth22_raw.csv",
       "summary_dir": "data/semisynth_transfer/results/summary",
-      "description": "Semi-synthetic transfer results over real monthly backgrounds; large downloaded source archives are excluded."
+      "description": "Benchmark-only 22-method semi-synthetic transfer results over real monthly backgrounds; ranking_paper_5metric_overall.csv is the extension ranking source and large downloaded source archives are excluded."
     },
     "post_rebuttal_second_expansion": {
       "real_physics_track_b": "data/post_rebuttal_second_expansion/real_physics_track_b",
@@ -48,7 +52,14 @@
       "generated_series": 300,
       "length": 512,
       "dt": 1.0,
-      "methods_preset": "core"
+      "methods": [
+        "ma_baseline",
+        "stl",
+        "ssa",
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+        "vmd",
+        "wavelet"
+      ]
     },
     "packaging_note": "Included for repository inspection and local benchmark execution, not published as a PyPI package release."
   }

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@@ -125,12 +167,12 @@
 "data/semisynth_transfer/results/summary/ranking_paper_5metric_overall.csv","1374","beacb7ce0f891080817d0935e3ae75f397bf6eefa194771bbe109f37379fff4f"
 "data/semisynth_transfer/results/summary/ranking_paper_5metric_raw.csv","3633854","392e2db3289f66db53491b6bf526c6e11fb81939d386fbe15aa6969e382ba148"
 "data/semisynth_transfer/results/summary/report.md","10240","14371f3b5f508883cee7ce2a87b7a45a279fa1de7c5650f1d3d1e636aa4a8fec"
+"data/semisynth_transfer/results/summary/semi22_vs_synth300_method_delta.csv","10196","41795c0b03558e90f3b90870c293f63c4a8510ab6bc022fe8c8b6a0edaeafb7a"
 "data/semisynth_transfer/results/summary/semi_transfer_legacy_by_mechanism_method.csv","11701","94dfbc5d5fa209ec01d800acbcc9bccb8bbf2407da69e35b5f69aafb2d695540"
 "data/semisynth_transfer/results/summary/semi_transfer_legacy_by_method.csv","3471","63b4ef3e6bd5137992a9772f1116223649605b32f0d5f8aee15dc011f3d08d75"
-"data/semisynth_transfer/results/summary/semi22_vs_synth300_method_delta.csv","10196","41795c0b03558e90f3b90870c293f63c4a8510ab6bc022fe8c8b6a0edaeafb7a"
+"data/synthetic_full22_extension/README.md","466","6d007273ba928e9f520feabc1c0ade02f06493035196aaf637f378655c43e260"
 "data/synthetic_full22_extension/configs/benchmark_manifest.json","4794","4f9bd8a13f21b2195fbcfa2a0435c864c45afb6459875ea5faba15eeaba6ed79"
 "data/synthetic_full22_extension/configs/method_defaults.json","7548","3b8165635ffe2c8da14779995c4b7db82a1dffde405ecbd3996581cb9cd6c04b"
-"data/synthetic_full22_extension/README.md","466","6d007273ba928e9f520feabc1c0ade02f06493035196aaf637f378655c43e260"
 "data/synthetic_full22_extension/results/merged/leaderboard_full22_raw.csv","3003283","61f67724cfe08e3ecbedce55b33f2728eb3c2de4fce25194dd91481c5d6461f6"
 "data/synthetic_full22_extension/results/summary/audit_12metric_by_scenario.csv","64378","52c1b6740bf24efa88d5aa8c6d2ed23758a79366d2ca382212f3190ec0d31e4c"
 "data/synthetic_full22_extension/results/summary/audit_12metric_by_tier.csv","30470","24642acae80ba4ac6ef83898815c3b47cb2a2e2d22bba64c84d1cbda73d2640e"
@@ -153,11 +195,9 @@
 "data/synthetic_full22_extension/results/summary/ranking_paper_5metric_scenarios.csv","10326","a194ec34aa349f7177f89c2ae2b328f9589e7919f003659c6a5f0497ad32e91e"
 "data/synthetic_full22_extension/results/summary/ranking_paper_5metric_tiers.csv","3511","06bd8784cd8dfba5c8ce1d15aa12b4b1ce800ae293e012e5d626257c6fcb679b"
 "data/synthetic_full22_extension/results/summary/report.md","12091","e7769cea54e0d0549add85f119aafdb11e933af5fd2cd09ec812f3639f75594a"
-"LICENSE","1087","b4a90b60dda4e329cead21d9c212995337ac4ebfebe5365939c4c9c19479f419"
-"metadata/dataset_schema.json","2557","77848315cc5cd3ceb3b9e39105548624e7f7aa3b2bc3241f11936e84862d0561"
-"metadata/release_manifest.json","2661","c624fdc2907755fe2984220e517b6d22b8b2ef02f5e9c40bf40b2cf054b5ef93"
-"README.md","8738","34e6df6ab0dbc89639864de3f71544e4654b064400cf27e8db8996fd64cea67c"
-"site_data/v1.0.0/evaluation_metrics.json","3899","1ea3d15c462a6122dbe328ab779cca9131350774d3ccea4142e6d483a420abb6"
+"metadata/dataset_schema.json","3291","135ebf889b90ffc48f66082a3985ec44ef6c41aac8020e37b744a7a11860bfae"
+"metadata/release_manifest.json","2923","77fa7b899340e197683462b0813f26d8b26a703ef4b27d0054401ffaf597c632"
+"site_data/v1.0.0/evaluation_metrics.json","4609","5b134e900a2308ad5d3a8c61f72909935c5d61de82350210458c2e978140b1d8"
 "site_data/v1.0.0/leaderboard_post_rebuttal_real_physics_track_b.csv","2213","02d55c80e7d63e5b907d39789614931c3dc0dcc73d97feaa0d25145f08e17637"
 "site_data/v1.0.0/leaderboard_post_rebuttal_real_proxy_track_c.csv","8146","956471fc5e08dad8b9bd2e14defb4a0d9d7561a3714c94340d89760397a09fe6"
 "site_data/v1.0.0/leaderboard_real_proxy22_overall.csv","1603","f215a601e5177b478e582c18b4eae41f65c8922901b3bb958f7a97bf0a8b0291"
@@ -165,5 +205,5 @@
 "site_data/v1.0.0/leaderboard_synthetic_full22_overall.csv","2750","b70da23760ca1f73e84732077e3586efcbcf259660422c27118929ae675fefbc"
 "site_data/v1.0.0/methods.json","2218","0a604519d6a6cda68d64e2a39969be66b8c4b76e7489d976d41fb5fab8a5e6e0"
 "site_data/v1.0.0/scenarios.json","842","ef72904caa04ab688768a8f066c7724f3826764fce6933401ca59bb1a191fc34"
-"site_data/v1.0.0/suites.json","860","00100d68d386c87f9475c3cc42afdb4df2294106ee1ae9ded4ff85b062f9466d"
+"site_data/v1.0.0/suites.json","870","5c14ac3d21f9965286a43912926f2a054be1630b9af0d111ba8c4ccaf0e27fd3"
 "upload_instructions.md","895","8155685c96d64a047dbb86c2264b75614b3be3af2bd6d03fd24aa04dace07ac1"

metadata/release_manifest.json

@@ -41,12 +41,12 @@
     "LaTeX build intermediates"
   ],
   "alignment_notes": [
-    "data/paper_tables contains exact CSV versions of camera-ready manuscript tables.",
+    "data/paper_tables contains exact CSV versions of camera-ready manuscript tables plus paper_core_50draw_leaderboard.csv and paper_core_50draw_by_tier.csv for direct Table 2 reproduction.",
     "data/paper_figures contains small paper figure assets for leaderboard visual alignment checks.",
-    "site_data/v1.0.0/evaluation_metrics.json records the five paper-core component-recovery metrics and the Table 2 display subset.",
-    "synthetic_full22_extension is an extension track and should not replace the primary standalone-decomposition interpretation.",
+    "site_data/v1.0.0/evaluation_metrics.json records the five paper-core component-recovery metrics, the Table 2 display subset, and the tier-balanced Table 2 aggregation rule.",
+    "synthetic_full22_extension is a benchmark-only 22-method track using the same 6-scenario, 50-draw synthetic protocol where applicable; it should not replace the primary six-family Table 2 / Figure 3 interpretation.",
     "real_proxy22 uses proxy diagnostics, not exact component recovery.",
-    "semisynth_transfer excludes large downloaded source files and keeps reproducible summaries plus small canonical backgrounds.",
+    "semisynth_transfer is a benchmark-only 22-method semi-synthetic transfer track; ranking_paper_5metric_overall.csv is the extension ranking source.",
     "post_rebuttal_second_expansion is later rebuttal-stage companion evidence; it does not replace the paper-aligned tables."
   ]
 }

site_data/v1.0.0/evaluation_metrics.json

@@ -1,9 +1,9 @@
 {
-  "protocol_id": "paper_classic_component_recovery",
+  "protocol_id": "paper_core_component_recovery_50draw",
   "paper_core_metric_count": 5,
   "paper_table2_display_metric_count": 2,
   "classic_view": "paper_global",
-  "calculation_unit": "Metrics are computed per generated draw after method outputs are aligned to (T_hat, S_hat, R_hat). Summary tables report arithmetic means over the relevant scenario, tier, or regime group. Coverage is the fraction of runs with successful outputs and valid metrics.",
+  "calculation_unit": "Metrics are computed per generated draw after method outputs are aligned to (T_hat, S_hat, R_hat). The camera-ready core protocol uses 6 scenarios x 50 deterministic draws at length 512. Scenario and tier summaries report arithmetic means over the relevant valid rows. Coverage is the fraction of runs with successful outputs and valid metrics.",
   "table2_aggregation": {
     "stationary_regime": "Tiers 1-2",
     "nonstationary_regime": "Tier 3",
@@ -11,6 +11,12 @@
       "metric_T_r2",
       "metric_S_spectral_corr"
     ],
+    "aggregation_rule": "Tier-balanced: compute method means separately for Tier 1, Tier 2, and Tier 3 over valid metric values. Stationary Table 2 columns are the equal-weight average of Tier 1 and Tier 2 means; non-stationary columns are Tier 3 means. Seasonal metrics are undefined for the trend-only scenario, so metric-specific valid-row counts are recorded in paper_core_50draw_by_tier.csv.",
+    "source_files": {
+      "raw_rows": "data/paper_tables/paper_core_50draw_leaderboard.csv",
+      "tier_means": "data/paper_tables/paper_core_50draw_by_tier.csv",
+      "display_table": "data/paper_tables/global_performance_summary.csv"
+    },
     "note": "Table 2 displays two paper-core metrics split by stationary and non-stationary regimes; Figure 3 uses the five-metric capability profile."
   },
   "implementation_defaults": {
@@ -66,10 +72,10 @@
     }
   ],
   "column_aliases": {
-    "stationary_trend_r2": "metric_T_r2 averaged over stationary regimes (Tiers 1-2)",
-    "stationary_seasonal_spectral_corr": "metric_S_spectral_corr averaged over stationary regimes (Tiers 1-2)",
-    "nonstationary_trend_r2": "metric_T_r2 averaged over non-stationary regimes (Tier 3)",
-    "nonstationary_seasonal_spectral_corr": "metric_S_spectral_corr averaged over non-stationary regimes (Tier 3)",
+    "stationary_trend_r2": "tier-balanced metric_T_r2: equal-weight average of Tier 1 and Tier 2 means",
+    "stationary_seasonal_spectral_corr": "tier-balanced metric_S_spectral_corr: equal-weight average of Tier 1 and Tier 2 means",
+    "nonstationary_trend_r2": "metric_T_r2 mean for Tier 3",
+    "nonstationary_seasonal_spectral_corr": "metric_S_spectral_corr mean for Tier 3",
     "metric_T_r2_mean": "metric_T_r2 arithmetic mean",
     "metric_T_dtw_mean": "metric_T_dtw arithmetic mean",
     "metric_S_r2_mean": "metric_S_r2 arithmetic mean",
@@ -77,7 +83,7 @@
     "metric_S_maxlag_corr_mean": "metric_S_maxlag_corr arithmetic mean"
   },
   "version_policy": {
-    "paper_classic": "Must match the camera-ready paper tables and figures.",
+    "paper_core_50draw": "Must match the camera-ready paper tables and figures.",
     "post_rebuttal_expansion": "May add methods, proxy diagnostics, and companion tracks, but must stay labelled as extension evidence."
   }
 }

site_data/v1.0.0/suites.json

@@ -7,7 +7,7 @@
   {
     "id": "synthetic_full22_extension",
     "name": "Synthetic Full22 Extension",
-    "description": "Paper-aligned 6-scenario synthetic extension with 22 method rows."
+    "description": "Benchmark-only 6-scenario, 50-draw synthetic extension with 22 method rows."
   },
   {
     "id": "real_proxy22",