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Record W4393769005 · doi:10.5281/zenodo.1009659

Countwordserror

2017· dataset· en· W4393769005 on OpenAlexaff
Jake Lever

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typedataset
Languageen
FieldComputer Science
TopicMachine Learning and Data Classification
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Results from tool executed using PubRunner on MEDLINE corpus. Code for the tool can be found at https://github.com/NCBI-Hackathons/PubRunner/tree/master/server/tools/CountWordsError/0.1

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)0.004
metaresearch head score (Gemma)0.020
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 score0.919
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0060.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0140.009
Science and technology studies0.0030.001
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0810.119

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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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
Published2017
Admission routes1
Has abstractyes

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