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Record W2554818531 · doi:10.7482/0003-9438-56-085

Instrumental colour measurement as a tool for light veal carcasses online evaluation

2013· article· en· W2554818531 on OpenAlexaff
A. Horcada, M. Juárez, A. Molina, M. Valera, M.J. Beriain

Bibliographic record

VenueArchives animal breeding/Archiv für Tierzucht · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAlberta Crop Industry Development Fund
Fundersnot available
KeywordsBreedCarcass weightAnimal scienceBiologyVeterinary medicineMathematicsBody weightMedicine

Abstract

fetched live from OpenAlex

Abstract. Eight-hundred and forty-five veal carcasses (535 males and 310 females) from Pirenaica breed and its crosses with Charolais and Limousin were used to compare carcass colour visual assessments 60 min post-mortem with the values obtained using a spectrocolorimeter (CIE Lab colour system) on the pectoralis superficialis muscle. Carcass weights ranged between 240 and 360 kg. Visual colour was subjectively evaluated by two trained assessors. The best correlation between visual appraisal and L* was observed in carcasses of 300–320 kg. A relatively strong correlation between visual appraisal and a* values was observed in carcasses of 280–300 kg of weight. However, the correlation between b* and visual appraisal was low. Using discriminant functions that included L* and a*, the spectrocolorimeter classified correctly 60 % of carcasses in the class colour assigned by the assessors. In fact, 99% of male and 96% of female carcasses were correctly assigned in the most common colour of veal carcasses in the Spanish market ('pink', 5). These results indicate that on-line instrumental colour measurements on pectoralis superficialis muscle could be recommended to objectively evaluate carcass colour of commercial veal for the Spanish market.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.872

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.300
Teacher spread0.220 · 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 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

Citations2
Published2013
Admission routes1
Has abstractyes

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