Record W4309915237 · doi:10.1002/sim.9071

Issue Information

2022· paratext· en· W4309915237 on OpenAlexaff

Stephen Raywood, Zaynah Tahmina Chowdhury, Sarah Maricris, A Sequerra, John Wiley, Ltd Sons, Kelly Kerridge, Theodore Colton, Andrew Johnson, Laurence S. Freedman, Robert W. Platt, Nigel Stallard, Lisa M. McShane, Els Goetghebeur, Sruthi Subramanyam, Rajasekhar Balasubramanian‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Usa Bandyopadhyay, Frank Bretz, Switzerland Cao, Canada Cole, Usa Collins, Uk Copas, Uk Desantis, Usa Ding, Usa Dobbin, Usa Duchateau, E. Gabriel, Sweden Halabi, Usa Harel, Usa Harrell, Usa He, Usa Hedeker, Usa Heinze, Chia-Chien Hsu, Usa Hu, Usa Hubbard, Usa Hung, Usa Ivanova, Usa Jacqmin-Gadda, France Jaki, Ук Канг, Usa Kelly, Australia Keogh, Minjun Kim, Usa Lawson, Usa Lee, L Liu, Usa Mclain, Usa Mcmahan, Usa O'malley, Carolyn M. Rutter, Usa Saarela, Canada Schaubel, Usa Schnitzer, C. Frank Shaw, Usa Siddique, Usa Sinha, Maarten van Smeden, Netherlands Tan, J Tebbs, Usa Tekwe, Usa Tian, Usa Todd, Uk Tsong, Usa Wang, Usa Wheeler, Usa Xiang, K Zou

Bibliographic record

VenueStatistics in Medicine · 2022

Typeparatext

Languageen

FieldEngineering

TopicTransportation Systems and Logistics

Canadian institutionsMcGill University

Fundersnot available

KeywordsComputer scienceCitationInformation retrievalLibrary science

DOI10.1002/sim.9071

Abstract

fetched live from OpenAlex

No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002

metaresearch head score (Gemma)0.013

Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated

Candidate categoriesInsufficient payload (model declined to judge)

Consensus categoriesInsufficient payload (model declined to judge)

DomainCandidate signal: none · Consensus signal: none

Study designCandidate signal: Not applicable · Consensus signal: Not applicable

GenreCandidate signal: Other · Consensus signal: Other

Teacher disagreement score0.078

Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

Category	Codex	Gemma
Metaresearch	0.002	0.013
Meta-epidemiology (narrow)	0.002	0.001
Meta-epidemiology (broad)	0.002	0.001
Bibliometrics	0.007	0.007
Science and technology studies	0.001	0.001
Scholarly communication	0.008	0.004
Open science	0.002	0.002
Research integrity	0.003	0.002
Insufficient payload (model declined to judge)	0.922	0.896

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 head0.013

GPT teacher head0.277

Teacher spread0.264 · 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