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Record W6927627634 · doi:10.3205/20ebm110

Evaluation der Qualität maschineller Übersetzungen von wissenschaftlichen (Abstracts) und laienverständlichen Zusammenfassungen (PLS) von Cochrane Reviews

2020· article· de· W6927627634 on OpenAlexaff

Bibliographic record

VenueGerman Medical Science (German Research Foundation) · 2020
Typearticle
Languagede
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCochrane
Fundersnot available
KeywordsCover (algebra)

Abstract

fetched live from OpenAlex

Hintergrund/Fragestellung: Hinter der anhaltenden Forderung nach „Knowledge Translation“ (Wissenstransfer) im Bereich von Gesundheitspolitik und -forschung [ref:1] steht der Wunsch, verfügbares, evidenzbasiertes Wissen zugänglich zu machen und transparent in die[zum vollständigen Text gelangen Sie über die oben angegebene URL]

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)0.264
metaresearch head score (Gemma)0.542
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2640.542
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0160.039
Bibliometrics0.0240.014
Science and technology studies0.0020.003
Scholarly communication0.0180.009
Open science0.0030.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.722
GPT teacher head0.630
Teacher spread0.091 · 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
DomainEvaluation
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

Citations0
Published2020
Admission routes1
Has abstractyes

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