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Record W2059447605 · doi:10.1002/sim.1973

Concomitant information in bioassay and semi-parametric estimation

2004· article· en· W2059447605 on OpenAlexafffund
Peter T. Kim, Christine H. Lee

Bibliographic record

VenueStatistics in Medicine · 2004
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsMcMaster UniversitySt. Joseph's HospitalSt. Joseph’s Healthcare HamiltonUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEstimatorInterval estimationParametric statisticsPoint estimationBioassaySmoothingSemiparametric modelStatisticsConfidence intervalComputer scienceEstimationMathematicsConcomitantEconometricsApplied mathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a flexible modern approach to handling concomitant information for estimating the relative potency parameter in quantitative bioassays. This is accomplished in a semi-parametric framework where the concomitant variable is included non-parametrically. Estimation is then performed using smoothing splines where the point and interval estimators of the relative potency parameter exhibits desirable asymptotic properties.

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)0.023
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.105
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0030.005
Open science0.0040.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.390
Teacher spread0.340 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2004
Admission routes2
Has abstractyes

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