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Record W4249491981 · doi:10.36019/9780813563893-010

Notes

2019· book-chapter· en· W4249491981 on OpenAlexfundno aff

Bibliographic record

VenueRutgers University Press eBooks · 2019
Typebook-chapter
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
FundersJanssen PharmaceuticalsU.S. National Library of MedicineNational Institute of Mental HealthUniversity of California, San DiegoJohns Hopkins UniversityUniversity of OxfordGoddard Space Flight CenterCincinnati Children's Hospital Medical CenterHarvard UniversityYork UniversityTulane UniversityWeill Cornell Medical CollegeUniversity of PennsylvaniaGeorge Washington UniversityNational Institutes of HealthUniversity of CambridgePrinceton University
KeywordsComputer science

Abstract

fetched live from OpenAlex

The laws have generated pediatric-related information on more than 400 drugs.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.595
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.5950.434

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 head0.035
GPT teacher head0.232
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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