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Une approche de localisation de symboles non-segmentés dans des documents graphiques

2009· article· en· W22063593 on OpenAlexvenueno aff
Thi-Oanh Nguyen, Salvatore Tabbone, Alain Boucher, Oriol Ramos Terrades

Bibliographic record

VenueTraitement du signal · 2009
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSymbol (formal)SpottingSearch engine indexingArtificial intelligencePattern recognition (psychology)Information retrieval

Abstract

fetched live from OpenAlex

Eighty four steers were randomly assigned to three pasture treatments with increasing levels of grain (T1: 0%; T2: 0.6%; T3: 1.2% of live weight) and to an ad libitum concentrate treatment, T4, to study the effects on carcass and meat quality. Animals were slaughtered with 500 kg of average live weight per treatment. Average daily gain increased with increasing levels of energy, determining different slaughter dates. Intermediate treatments showed higher carcass weight than T1. T4 and T3 had a higher weight of valuable cuts than T1 and T4. Pistolas from T4 had a higher fat proportion and lower bone percentage. Increasing levels of energy in diet decreased fat yellowness. After 20 days of aging, T4 had the lowest muscle a(∗) values and shear force was higher for T4 than for T1. With pastures finishing strategy, no adverse effects on meat quality were detected and tenderness was enhanced.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2009
Admission routes1
Has abstractyes

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