MétaCan
Menu
Back to cohort
Record W36713129 · doi:10.3168/jdsc.2022-0231

Análisis computacional de imágenes de resonancia magnética para diferenciar jamones ibéricos en función de la alimentación de los cerdos

2010· article· en· W36713129 on OpenAlexfundno aff
María Trinidad Pérez Palacios, Teresa Antequera, Antonio Sánchez, Rubén Molano Gómez, Ramón Ignacio Palacios Bote

Bibliographic record

VenueEurocarne: La revista internacional del sector cárnico · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta MilkUniversity of Guelph
KeywordsHumanitiesPhysicsMolecular biologyArtBiology

Abstract

fetched live from OpenAlex

En este trabajo se propone el uso de una aplicacion informatica desarrollada para el analisis de imagenes de resonancia magnetica de jamones, con el objetivo de clasificar los jamones ibericos en funcion de la alimentacion recibida por los cerdos durante la fase de cebo (montanera vs. piensos alto oleico).

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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 designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEurocarne: La revista internacional del sector cárnicoSame topicMeat and Animal Product QualityFrench-language works237,207