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Record W2916992868 · doi:10.1097/jfn.0000000000000171

Journal of Forensic Nursing 2016 Articles of the Year

2017· article· en· W2916992868 on OpenAlexaffabout
Cindy Peternelj‐Taylor

Bibliographic record

VenueJournal of Forensic Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsForensic nursingForensic scienceMedicinePsychologyNursingVeterinary medicine

Abstract

fetched live from OpenAlex

Author Affiliations: College of Nursing, University of Saskatchewan. The author declares no conflict of interest. Correspondence: Cindy Peternelj-Taylor, RN, BScN, MSc, DF-IAFN, College of Nursing, University of Saskatchewan, 4214-104 Clinic Place, Saskatoon, SK, Canada S7N 2Z4. E-mail: [email protected].

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.327
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.3270.106

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 head0.032
GPT teacher head0.319
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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 routes2
Has abstractyes

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