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Record W4394145704 · doi:10.6084/m9.figshare.21256743

Additional file 2 of A standardized nomenclature for mammalian histone genes

2022· dataset· en· W4394145704 on OpenAlexaff
Ruth L. Seal, Paul Denny, Elspeth A. Bruford, Anna K. Gribkova, David Landsman, William F. Marzluff, Monica McAndrews, Anna R. Panchenko, Alexey К. Shaytan, Paul B. Talbert

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsQueen's University
Fundersnot available
KeywordsNomenclatureBiologyComputational biologyGeneticsGeneZoologyTaxonomy (biology)

Abstract

fetched live from OpenAlex

Additional file 2. List of human histone proteins from the Histone Sequence Database for every histone gene, the available set of transcript and coding sequence GENCODE annotations were obtained from the Ensembl 105 database. Next, only protein-coding transcripts identical between Ensembl automated annotation and HAVANA manual curation were retained. In those cases where several transcripts of one gene correspond to the same amino acid protein sequence, only one record was retained with preference given to those that match NCBI’s RefSeq annotation. For every histone gene the list includes the HGNC symbol, information about corresponding protein sequences, their length, accession numbers within NCBI and Ensembl resources, as well as a list of relevant literature references in the form of PubMed identifiers. For a few histone variant genes, the attribution to a certain histone variant class has not yet been clearly established in the literature; these are marked in the list by a question mark.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.494
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4940.173

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.265
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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