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Editorial Comment

2019· editorial· es· W4246793131 on OpenAlexaffabout
Jason Izard, D. Robert Siemens

Bibliographic record

VenueThe Journal of Urology · 2019
Typeeditorial
Languagees
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

No AccessJournal of UrologyAdult Urology1 Aug 2019Editorial CommentThis article comments on the following:Risk of Bone Fractures Following Urinary Intestinal Diversion: A Population Based Study Jason P. Izard and D. Robert Siemens Jason P. IzardJason P. Izard Department of Urology, Queen’s University, Toronto, OntarioCanada More articles by this author and D. Robert SiemensD. Robert Siemens Department of Urology, Queen’s University, Toronto, OntarioCanada More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000558493.48479.98AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 202(2), pp. 324–325 © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of Urology8 Jul 2019Risk of Bone Fractures Following Urinary Intestinal Diversion: A Population Based Study Volume 202Issue 2August 2019Page: 324-325 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jason P. Izard Department of Urology, Queen’s University, Toronto, OntarioCanada More articles by this author D. Robert Siemens Department of Urology, Queen’s University, Toronto, OntarioCanada More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.005
metaresearch head score (Gemma)0.049
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.409
Threshold uncertainty score0.843

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.4090.251

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 head0.012
GPT teacher head0.280
Teacher spread0.269 · 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
GenreEditorial

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
Published2019
Admission routes2
Has abstractyes

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