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Record W4206544501 · doi:10.1002/ana.26171

Issue Information

2021· paratext· en· W4206544501 on OpenAlexaboutno aff
Jan D. Lünemann, Sunny Malhotra, Mari L. Shinohara, Xavier Montalbán, Manuel Comabella, Seward B. Rutkove, Rebecca A. Betensky, Aarno Palotie, Emer O’Connor, Carmen Fourier, Caroline Ran, Prasanth Sivakumar, Franziska Liesecke, Laura Southgate, Aster V. E. Harder, Lisanne S. Vijfhuizen, Janice Yip, Nicola Giffin, Nicholas Silver, Fayyaz Ahmed, Isabel C. Hostettler, Brendan Davies, M. Zameel Cader, Benjamin S. Simpson, Roisin Sullivan, Stéphanie Efthymiou, Joycee Adebimpe, Olivia Quinn, Ciarán Campbell, Gianpiero L. Cavalleri, Michail Vikelis, Tim Kelderman, Koen Paemeleire, Emer Kilbride, Lou Grangeon, Susie Lagrata, Daisuke Danno, Richard C. Trembath, Nicholas Wood, Ingrid Kockum, Bendik S. Winsvold, Anna Steinberg, Christina Sjöstrand, Elisabet Waldenlind, Jana Vandrovcová, Henry Houlden, Manjit Matharu, Andrea Carmine Belin, Raymond Noordam, Sigrid Børte, Lisette J. A. Kogelman, Irene de Boer, Erling Tronvik, Frits R. Rosendaal, Ko Willems van Dijk, Laurent F. Thomas, Espen Saxhaug Kristoffersen, Rolf Fronczek, Patricia Pozo‐Rosich, Rigmor Jensen, Michel D. Ferrari, Thomas Folkmann Hansen, John‐Anker Zwart, Gisela M. Terwindt, Arn van den Maagdenberg

Bibliographic record

VenueAnnals of Neurology · 2021
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnnalsCitationComputer scienceLibrary scienceInformation retrievalHistoryClassics

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.Follow our progress at www.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.085
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0080.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9150.904

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 head0.088
GPT teacher head0.267
Teacher spread0.179 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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