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Record W4389063115 · doi:10.51387/23-nejsds13edi

Editorial. Design and Analysis of Experiments for Data Science

2023· article· en· W4389063115 on OpenAlexaff
HaiYing Wang, Xinwei Deng, Devon Lin, Ming‐Hui Chen, Minge Xie, Jing Wu

Bibliographic record

VenueThe New England Journal of Statistics in Data Science · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsQueen's University
Fundersnot available
KeywordsData scienceStatistical analysisNew englandLibrary scienceComputer scienceStatisticsPolitical scienceMathematicsLaw

Abstract

fetched live from OpenAlex

Publisher: New England Statistical Society, Journal: The New England Journal of Statistics in Data Science, Title: Editorial. Design and Analysis of Experiments for Data Science, Authors: HaiYing Wang, Xinwei Deng, Devon Lin

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)0.025
metaresearch head score (Gemma)0.117
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.028
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.117
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0070.003
Science and technology studies0.0040.003
Scholarly communication0.0080.004
Open science0.0050.001
Research integrity0.0120.020
Insufficient payload (model declined to judge)0.0280.023

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 head0.392
GPT teacher head0.539
Teacher spread0.147 · 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
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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