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Record W4407136535 · doi:10.1080/29984688.2025.2449780

Introducing <i>Journal of Applied Statistics: Environmental Statistics and Data Science</i>

2025· article· en· W4407136535 on OpenAlexaff
Nathaniel K. Newlands

Bibliographic record

VenueJournal of Applied Statistics Environmental Statistics and Data Science · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsUniversity of Victoria
FundersAugusta University
KeywordsStatisticsSummary statisticsData scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

I am honored to assume the position of Editor-in-Chief of Journal of Applied Statistics: Environmental Statistics and Data Science (JAS:ESDS), a new companion journal affiliated with an older, esta...

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)0.022
metaresearch head score (Gemma)0.110
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 score0.033
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.110
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.008
Science and technology studies0.0020.004
Scholarly communication0.0140.006
Open science0.0020.002
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0330.039

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 head0.050
GPT teacher head0.359
Teacher spread0.309 · 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
Published2025
Admission routes1
Has abstractyes

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