MétaCan
Menu
Back to cohort
Record W3092618987 · doi:10.3917/lige.181.0143

Le mème comme descendance prolifique du Net.Art

2020· article· fr· W3092618987 on OpenAlexaff
Thibaut Vaillancourt

Bibliographic record

VenueLigeia · 2020
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Colorectal cancer is the third leading cause of cancer deaths worldwide, and its incidence is rising. Colonoscopy prevents the onset of colorectal cancer and reduces associated mortality, thanks to the endoscopic resection of polyps, particularly adenomatous polyps. However, adenomatous polyps are not the only type of polyp, and their resection can vary according to several factors: size, shape, degenerative aspect, suspicion of submucosal invasion, etc.Significant technical advances (endoscopic submucosal dissection, artificial intelligence, etc.) have been made in recent years. Where do we stand? It is essential to use the appropriate classification tools to characterize colonic polyps in order to best guide the type of resection.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.333
GPT teacher head0.288
Teacher spread0.045 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueLigeiaSame topicCultural Insights and Digital ImpactsFrench-language works237,207