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Record W6926300273 · doi:10.25384/sage.21213746

Supplemental Material - Canadian Association of Radiologists/Canadian Association for Interventional Radiology/Canadian Society of Thoracic Radiology Guidelines on Thoracic Interventions

2023· article· en· W6926300273 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsAssociation (psychology)Psychological interventionInterventional radiologyProfessional associationMEDLINE

Abstract

fetched live from OpenAlex

Supplementary Material for Canadian Association of Radiologists/Canadian Association for Interventional Radiology/Canadian Society of Thoracic Radiology Guidelines on Thoracic Interventions by Elsie T. Nguyen, Hamid Bayanati, Casey Hurrell, Matthew Aitken, Edward M. Cheung, Ashish Gupta, Scott Harris, Tony Sedlic, Jana Lyn Taylor, Gerald Gahide, and Carole Dennie in Canadian Association of Radiologists Journal.

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)0.028
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: Methods · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6160.202

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 head0.127
GPT teacher head0.408
Teacher spread0.281 · 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
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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