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Record W2309340362 · doi:10.1007/978-0-8176-4542-7

Advances in Statistical Methods for the Health Sciences

2007· book· en· W2309340362 on OpenAlexaff
Jean-Louis Auget, Natarajan Balakrishnan, Mounir Mesbah, Geert Molenberghs

Bibliographic record

VenueBirkhäuser Boston eBooks · 2007
Typebook
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsManagement scienceComputer scienceData scienceEngineering

Abstract

fetched live from OpenAlex

1 Regression models 21 2.3.2 Classification and regression trees (CART) 22 2.3.3 Formation of risk groups 23 2.4 Results in the GBSG-2 Study 23 2.4.1 Regression models -standard applications 23 2.4.2 Regression models -the MFP-approach 25 2.4.3 Summary assessment -implication of the modelling strategy 25 2.4.4 Application of classification and regression trees 28 2.5 Formation and Validation of Risk Groups 30 2.6 Discussion 32

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)0.011
metaresearch head score (Gemma)0.037
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: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.037
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.008
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0240.026

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 head0.434
GPT teacher head0.585
Teacher spread0.151 · 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
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

Citations62
Published2007
Admission routes1
Has abstractyes

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