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Record W4381094562 · doi:10.14814/phy2.15321

Issue Information

2023· paratext· en· W4381094562 on OpenAlexfundno aff

Bibliographic record

VenuePhysiological Reports · 2023
Typeparatext
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignLudwig-Maximilians-Universität MünchenAnhui Medical UniversityChinese Academy of SciencesGöteborgs UniversitetOhio State UniversityNational University of SingaporeUniversity College LondonVanderbilt University Medical CenterMidwestern UniversityUniversity of PittsburghUniversity of BathRoy J. and Lucille A. Carver College of Medicine, University of IowaBrody School of MedicineJohns Hopkins UniversitySouthern Medical UniversityTurun YliopistoAarhus UniversitetUniversity of BristolUniversity of KansasSchool of Medicine, Boston UniversityEast Carolina UniversityUniversity of OtagoAugusta UniversityBrock UniversityUniversity of MiamiMcMaster UniversityNanjing Medical UniversityVanderbilt UniversityUniversiteit StellenboschBrown University
KeywordsCitationComputer scienceInformation retrievalWorld Wide WebLibrary scienceData science

Abstract

fetched live from OpenAlex

Physiological Reports is an online only, open access journal that will publish peer-reviewed research across all areas of basic, translational and clinical physiology and allied disciplines.Physiological Reports is a collaboration between The Physiological Society and the American Physiological Society, and is therefore in a unique position to serve the international physiology community through quick time to publication while upholding a quality standard of sound research that constitutes a useful contribution to the fi eld.Papers will be accepted solely on the basis of scientifi c rigor, adherence to technical and ethical standards, and evidence that the study is suffi ciently well-conceived and the data support the conclusions.Physiological Reports is interested in cellular and molecular studies as well as intact tissues, model organisms and translational studies.We welcome the work of biomedical scientists, whose studies incorporate physiological fi ndings.Papers with negative data or confi rmatory results are acceptable as long as they are well conceived.Papers concerned with modeling or new methods are acceptable as long as they are of physiological interest.

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 score0.140
Threshold uncertainty score0.200

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 studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.8600.801

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 head0.237
GPT teacher head0.435
Teacher spread0.198 · 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
Published2023
Admission routes1
Has abstractyes

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