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Record W3027736642 · doi:10.22237/jmasm/1571745720

On Statistical Significance of Discriminant Function Coefficients

2020· article· en· W3027736642 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Modern Applied Statistical Methods · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsUniversity of ManitobaUniversity of Calgary
Fundersnot available
KeywordsMathematicsDiscriminantDiscriminant function analysisLinear discriminant analysisStatisticsMultivariate statisticsStatistical hypothesis testingOptimal discriminant analysisStatistical significanceFunction (biology)Applied mathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Discriminant function coefficients are useful for describing group differences and identi- fying variables that distinguish between groups. Test procedures were compared based on asymptotically approximations, empirical, and exact distributions for testing hypotheses about discriminant function coefficients. These tests are useful for assessing variable impor- tance in multivariate group designs.

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)0.008
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.540
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
GPT teacher head0.486
Teacher spread0.289 · 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