MétaCan
Menu
Back to cohort
Record W2185502339 · doi:10.1255/nirn.1564

Could near Infrared Spectra of Ears Be Used to Classify Carcass Composition in Pigs?

2015· article· en· W2185502339 on OpenAlexafffundabout
N. Prieto, M. Juárez, M.E.R. Dugan, Ó. López-Campos, R. T. Zijlstra, J.L. Aalhus

Bibliographic record

VenueNIR news · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Livestock and Meat AgencySwine Innovation Porc
KeywordsComposition (language)Animal scienceInfraredChemistryFood scienceMaterials scienceBiologyOpticsPhysics

Abstract

fetched live from OpenAlex

Nuria Prieto, Manuel Juarez, Michael E.R. Dugan, Oscar Lopez-Campos, Ruurd T. Zijlstra and Jennifer L. Aalhus Lacombe Research & Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta, T4L 1W1, Canada. E-mail: nuria.prieto@agr.gc.ca Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture/Forestry Centre, Edmonton, Alberta, T6G 2P5, Canada

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.238

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 communication0.0000.000
Open science0.0000.000
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 head0.142
GPT teacher head0.305
Teacher spread0.163 · 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

Citations0
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueNIR newsSame topicMeat and Animal Product QualityFrench-language works237,207