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Record W1977586926 · doi:10.1191/0962280202sm289ra

Analysis of data with excess zeros

2002· article· en· W1977586926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueStatistical Methods in Medical Research · 2002
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsnot available
Fundersnot available
KeywordsBiometricsWilcoxon signed-rank testEconometricsStatisticsStatistical hypothesis testingStatistical powerTest (biology)Statistical modelComputer scienceData setStatistical analysisMathematicsArtificial intelligenceMann–Whitney U test

Abstract

fetched live from OpenAlex

This paper is an introduction to a set of papers on two-part models that were presented at the 2000 Joint Statistical Meetings of American Statistical Association, International Biometric Society (ENAR), International Biometric Society (WNAR), Institute of Mathematical Statistics, and Statistical Society of Canada. The second part of the paper summarizes results of a study of the size and power of two-part models. It proposes that two-part models are a useful alternative to the t-test and the Wilcoxon test.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.199
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.605
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.199
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.000

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 head0.640
GPT teacher head0.672
Teacher spread0.032 · 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