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Record W4243873126 · doi:10.1080/21681163.2019.1594056

Editorial

2019· editorial· en· W4243873126 on OpenAlexfundno aff
Gustavo Carneiro, João Manuel R. S. Tavares, Andrew P. Bradley, João Paulo Papa, Jacinto C. Nascimento, Jaime S. Cardoso, Zhi Lu, Vasileios Belagiannis

Bibliographic record

VenueComputer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2019
Typeeditorial
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMultiple Sclerosis Society of Canada
KeywordsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Deep learning methods have experienced an immense growth in interest from the medical image analysis community, particularly in the last years. The main reasons behind this interest lie in ability ...

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)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.079
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.001
Science and technology studies0.0030.001
Scholarly communication0.0080.003
Open science0.0020.003
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0790.058

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 head0.009
GPT teacher head0.348
Teacher spread0.339 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractno

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