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Record W2493963530 · doi:10.1007/b138793

Advances in Ranking and Selection, Multiple Comparisons, and Reliability

2005· book· en· W2493963530 on OpenAlexaff
N. Balakrishnan

Bibliographic record

VenueBirkhäuser Boston eBooks · 2005
Typebook
Languageen
FieldDecision Sciences
TopicMulti-Criteria Decision Making
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRanking (information retrieval)Selection (genetic algorithm)Reliability (semiconductor)Computer scienceStatisticsReliability engineeringMachine learningMathematicsEngineering

Abstract

fetched live from OpenAlex

13.1 Introduction and General Overview 215 13.2 Early Approaches 218 13.2.1 The limits of agreement (LOA) approach 219 13.2.2 Intraclass correlation and related measures 222 13.2.3 Concordance correlation approach 224 13.3 Recent Developments 226 13.3.1 Approaches based on percentiles and coverage probability 226 13.3.2 Approaches based on the intersection-union principle 229 13.4 An Example 232 13.5 Selection Problems in Measuring Agreement 235 13.5.1 Selection of the best 236 13.5.2 Assessment of agreement and selection of the best 238 13.

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)0.016
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.010
Science and technology studies0.0010.005
Scholarly communication0.0050.006
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0160.008

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 head0.081
GPT teacher head0.382
Teacher spread0.301 · 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

Citations67
Published2005
Admission routes1
Has abstractyes

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