MétaCan
Menu
Back to cohort
Record W2010044725 · doi:10.3917/docsi.421.0012

Scénarios de production pour l'indexation d'images animées

2005· article· fr· W2010044725 on OpenAlexaff
James M. Turner, Emmanuël Colinet

Bibliographic record

VenueDocumentaliste-Sciences de l Information · 2005
Typearticle
Languagefr
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsIndexationHumanitiesPhilosophyEconomics

Abstract

fetched live from OpenAlex

Résumé Cette étude s’inscrit dans une série de travaux relatifs à la génération automatique d’indexation d’images animées. Des supports textuels connexes à la production audiovisuelle (scénarios de production, sous-titres pour malentendants, audiovision pour malvoyants) sont ici utilisés à des fins d’extraction de vocabulaire permettant l’indexation de films séquence par séquence. Le contexte de cette recherche, la méthode adoptée et les résultats obtenus à partir d’un exemple précis sont présentés et discutés. Ce travail offre une base théorique solide en faveur de l’exploitation comme matériel-source de textes créés lors des processus de préproduction, de production et de postproduction de documents audiovisuels.

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)0.007
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.025
GPT teacher head0.322
Teacher spread0.297 · 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
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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueDocumentaliste-Sciences de l InformationSame topicNatural Language Processing TechniquesFrench-language works237,207