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metadata 
pretty_name: XV-ML full-ledger paper classification
tags:
  - papers
  - machine-learning
  - research-metadata
  - provenance
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet
  - config_name: curated_references
    data_files:
      - split: train
        path: coverage/references.parquet
  - config_name: review_candidates
    data_files:
      - split: train
        path: review/data/*.parquet

XV-ML full-ledger paper classification

This is the modelomics/xv-ml release, generated from a captured full-ledger pass. Release modelome-xv-ml-2026-10-08 is complete and ready for release. The manifest is the source of runtime totals; this card does not invent counts or describe an incomplete manifest as published. The direct dataset is modelomics/xv-ml.

The capture is bounded by the recorded live-ledger high-water mark. It is a full pass over that current ledger with curated supplements, not a global coverage claim; unresolved source gaps remain possible.

Storage and counts

Parquet files use the canonical train storage split. This is a storage name, not a training/evaluation benchmark claim.

Measure Rows
classifier_included 1530803
curated_only 2047
examined_rows 33323692
included_rows 1532850
review_rows 8429993
runner_included_rows 9960930
scored_rows 33313758
Source Rows
acl-test-of-time 13
arxiv-complete-snapshot 700358
biorxiv 2832
crossref 34183
crossref-public-data 56
cvpr-longuet-higgins 5
daturkel-landmark-ml-papers 74
dblp-xml-snapshot 378818
epoch-all-ai-models 1726
europe-pmc 49201
gh-ml-hf-daily-papers 4
gh-ml-repository-doi-refs 14
hf-ml-doi-snapshot 1
hf-ml-snapshot 9
icml-test-of-time 9
medrxiv 390
neurips-test-of-time 3
openalex-snapshot-crossfield-ml 19696
openalex-snapshot-cs 344805
openalex-works 593
pubmed-bulk 5
spdin-deep-learning-chronology 55
Record type Rows
blog 152
cited_document 1168
model_card 248
paper 1531068
technical_report 214
Metadata status Rows
(not reported) —

Chronology and coverage are recorded by the run manifest:

| (not reported) | — |

Row schema

Rows preserve title, abstract, DOI or arXiv URL, source, source revision, source license, article license, group identity, exact text hash, model score, model inclusion, final inclusion, inclusion reason, classifier or curated reference provenance, text basis, language, and eligibility status. Each row also has record_type (paper, technical_report, model_card, announcement/blog, or cited_document) and metadata_status (corpus_metadata, corpus_matched, hydrated, citation_only, or unavailable). Text basis is one of title_abstract, title_only, abstract_only, or no_text.

This is a broad ML-research dataset, not a claim that every primary record is a conventional paper. Curated direct-document references remain typed even when they have no DOI, using a stable source URL key and metadata-availability flags. A missing document URL remains an audit configuration such as no_document_reference; it does not create a guessed paper. An explicit citation without a URL can still retain its reference_label, title, authors, year, stable source key, and metadata-availability flag.

Curated references are post-hoc additions retained only after an explicit curator source audit. A curated override is provenance, not evidence that the classifier has 100% recall. Rows with inclusion_reason=curated_reference make that override explicit. Curated scholarly references from Epoch and picked chronologies are retained irrespective of the classifier decision.

The high-recall policy is high_recall_with_review_v1: scorer threshold 0.35, primary/default inclusion threshold 0.5, and every scorer-positive row is retained in the audit collection. Curated references override the score. Non-curated rows in the [0.35, 0.5) band are retained in the optional review_candidates config with included=false, inclusion_reason=review_candidate, and review flags. Language and title-only uncertainty are flags, not vetoes. This review is not a population statistic or an accuracy estimate.

Classifier provenance

Field Value
rubric_version ml-methods-neural-apps-v1
model_version paper-ml-tfidf-logreg-v1
model_sha256 df506fb1480a729f827f9825714b330a251499a07cdb1d223c5405095ac5e8a0
threshold 0.35
language_policy English normlangid confidence >= 0.90 is a review flag after model scoring; it does not veto model-positive inclusion
provenance.evaluation_counts 79
provenance.language_detector langid-1.1.6-normalized
provenance.training_counts 320
provenance.training_fingerprint 0d050304377d4727ccf627947c7bc33b76f66710ae6b6852521000864599554c
Release metadata Value
dataset_scope broad_ml_research_v1
inclusion_policy high_recall_with_review_v1

The current classifier was trained with 320 assistant-origin training annotations and evaluated on 79 assistant-origin evaluation annotations under the earlier scope. It remains a coverage anchor for blind spots; it does not define the recall-first inclusion policy. Those metrics are agreement checks and are not validated accuracy, human gold evaluation, or a population estimate. Title-only and language-uncertain rows are retained with flags.

Licensing and reproducibility

Source-specific licenses are preserved per row. This card assigns no global redistribution license, and the release contains paper metadata/abstract fields rather than paper full text. Keep the immutable manifest, captured high-water mark, source revisions, model checksum, chunk receipts, and any curation audit together when reproducing the run.

Files declared by the manifest:

  • README.md
  • coverage/references.parquet
  • coverage/summary.json
  • data/part-00000.parquet
  • data/part-00001.parquet
  • data/part-00002.parquet
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  • data/part-00010.parquet
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  • data/part-00044.parquet
  • data/part-00045.parquet
  • data/part-00046.parquet
  • provenance/model-manifest.json
  • provenance/source-manifest.json
  • review/data/part-00000.parquet
  • review/data/part-00001.parquet
  • review/data/part-00002.parquet
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  • review/data/part-00010.parquet
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  • review/data/part-00019.parquet
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