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Record W4253096177 · doi:10.1002/chir.22996

Issue Information

2019· paratext· en· W4253096177 on OpenAlexaff
D Nina, Chirality Mirasol, Matela Prof, William T. Daniel, Wilma James, Jeanne Crassous, Di Lorenzo, George A. Ellestad, Karl‐Heinz Ernst, Joseph-Nathan Pedro, Kurtán Tibor, Weiping Liu, A Laurence, Poopalasingam Sankar, Xinhua Wan, Christopher J. Welch, Javix Thomas, Zhaoqing Xu, Eiji Yashima, Zhiqiang Zhou, Orsola Baydoun, Thierry Buffeteau, Nicolas Daugey, Marion Jean, Nicolas Vanthuyne, Laure‐Lise Chapellet, Nicolas De Rycke, Thierry Brotin, Qing Tang, Lei Zhao, Jian Xie, Kui Liu, Song Zhou, Yuliang Zhao, Suqing Li, Xiaoyang Wang, Jun Yu, Yang Song, Xiaoyu Guo, Jia‐Lin Xu, F.-L Li, Ya‐Ping Xue, Y.-G Zheng, Michela Vargiu, Lucilla Favero, Andrea Menichetti, Valeria Di Bussolo, Fabio Marchetti, Gennaro Pescitelli, Di Su, Mauro Pineschi, David Esteban Quintero Jimenez, Juliana Cristina Barreiro, F Dos Santos, S De Vasconcellos, Ana Lúcia Figueiredo Porto, João M. Batista

Bibliographic record

VenueChirality · 2019
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

No abstract is available for this article.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9420.873

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 head0.040
GPT teacher head0.226
Teacher spread0.186 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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