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Record W4249092028 · doi:10.1002/dneu.22443

Issue Information

2017· paratext· en· W4249092028 on OpenAlexaff
David A. Brafman, Karl Willert, George Smith, Gianluca Gallo, Ruxandra F. Sîrbulescu, Annette Meyer, G€ Unther, Kaja Zupanc, Ailen S. Cervino, Dante A. Paz, Jimena Laura Frontera, Amogh P. Belagodu, Stephen A. Fleming, Roberto Galvez, Eray Mehmet, Halef Alcigir, Sevil Dogan, Fatma Vural, Joe Tomaszewski, Darcy B. Kelley, Moses V. Chao, Eduardo R. Macagno, J. W. Fawcett, Carlos D. Aizenman, Peter W. Baas, Perry F. Bartlett, Benedikt Berninger, Laura N. Borodinsky, Salvatore Carbonetto, Alain Chédotal, Hollis T. Cline, Wen‐Biao Gan, Joel C. Glover, Sarah Guthrie, Volker Hartenstein, Christine E. Holt, Nancy Y. Ip, Kozo Kaibuchi, Paul C. Letourneau, Zhen‐Ge Luo, Elisa Martı́, Cory T. Miller, Kenneth Muller, Keith Murai, Fujio Murakami, Alberto E. Pereda, Andreas Prokop, Hitoshi Sakano, Jerry Silver, Esther T. Stoeckli, Ron Stoop, Michèle Studer, Henry Sun, Zhi‐Qi Xiong, Xian Yu, Yimin Zou, Stephanie Serraon

Bibliographic record

VenueDevelopmental Neurobiology · 2017
Typeparatext
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsMcGill University
Fundersnot available
KeywordsCitationInformation retrievalLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Wiley's Corporate Citizenship initiative seeks to address the environmental, social, economic, and ethical challenges faced in our business and which are important to our diverse stakeholder groups. Since launching the initiative, we have focused on sharing our content with those in need, enhancing community philanthropy, reducing our carbon impact, creating global guidelines and best practices for paper use, establishing a vendor code of ethics, and engaging our colleagues and other stakeholders in our efforts.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.163

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 head0.025
GPT teacher head0.260
Teacher spread0.236 · 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; both teacher heads 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
Published2017
Admission routes1
Has abstractyes

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