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Record W4294339225 · doi:10.54846/jshap/1291

Maximizing value and minimizing waste in swine research: Availability and accessibility of research reports

2022· article· en· W4294339225 on OpenAlexafffund
Jan M. Sargeant, Annette M. O’Connor, Terri L. O’Sullivan, Alejandro Ramirez, Ali Versluis

Bibliographic record

VenueJournal of Swine Health and Production · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsPresentation (obstetrics)Value (mathematics)Computer scienceOperations researchMedicineEngineeringSurgery

Abstract

fetched live from OpenAlex

To be useful for decision-making, research results need to be available. This means that full reports (methods and results) for trials need to be published, preferably in a journal. However, there is evidence that only a small proportion of swine trials presented at conferences are subsequently published in journals. This is problematic, as results may differ between a conference presentation and journal publication. Published results also need to be accessible, either through open-access or traditional journals or through other sources that do not violate copyright agreements. Researchers should strive to make full research reports widely available.

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)0.422
metaresearch head score (Gemma)0.658
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.712

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4220.658
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0160.012
Science and technology studies0.0030.008
Scholarly communication0.0240.027
Open science0.0050.011
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0160.007

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 head0.149
GPT teacher head0.437
Teacher spread0.287 · 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 designObservational
DomainReporting
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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