MétaCan
Menu
Back to cohort
Record W2401375860 · doi:10.1016/j.jtbi.2017.01.032

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity

2017· article· en· W2401375860 on OpenAlexaff
Elizabeth C. Lee, Michael R. Kelly, Brad M. Ochocki, Segun M. Akinwumi, Karen E. S. Hamre, Joseph H. Tien, Marisa C. Eisenberg

Bibliographic record

VenueJournal of Theoretical Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Alberta
FundersDivision of Biological InfrastructureNational Institute of General Medical SciencesNational Institute for Mathematical and Biological SynthesisWorld Health OrganizationNational Institutes of HealthNational Science Foundation
KeywordsRobustness (evolution)InferenceComputer scienceContext (archaeology)StatisticsRange (aeronautics)EconometricsMathematicsArtificial intelligenceBiologyEngineering

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)0.031
metaresearch head score (Gemma)0.194
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.194
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.001
Science and technology studies0.0020.007
Scholarly communication0.0050.010
Open science0.0050.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.329
Teacher spread0.309 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations30
Published2017
Admission routes1
Has abstractno

Explore more

Same venueJournal of Theoretical BiologySame topicVibrio bacteria research studiesFrench-language works237,207