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Record W4241396151 · doi:10.1016/s1701-2163(17)30047-6

Guide for Authors English

2017· article· en· W4241396151 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2017
Typearticle
Languageen
FieldMedicine
TopicMedical Case Reports and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical physics

Abstract

fetched live from OpenAlex

The Journal of Obstetrics and Gynaecology Canada (JOGC) publishes original articles and systematic reviews in the areas of gynaecology, obstetrics, reproductive endocrinology, gynaecologic oncology, women's health, maternal fetal medicine, urogynaecology, infectious diseases, genetics, pediatric and adolescent gynaecology, diagnostic imaging, ethical and legal issues, and medical and public education.The journal also publishes Guidelines from The Society of Obstetricians and Gynaecologists of Canada (SOGC), Images of the Month, Case Reports, Commentaries and Letters to the Editor.Only exclusive submissions will be considered for publication. LanguageThe languages of JOGC are English and French.For English spelling, we follow the Canadian Oxford Dictionary. 1 Authors are required to use generic or chemical names of pharmaceuticals rather than specific brand or trade names.

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)0.007
metaresearch head score (Gemma)0.062
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 score0.635
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.004
Science and technology studies0.0020.001
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.6350.644

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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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 routes1
Has abstractyes

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