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Record W2466023166 · doi:10.3390/jimaging2030021

Assessing Consistency in Radiated Thermal Output of Beef Steers by Infrared Thermography

2016· article· en· W2466023166 on OpenAlexaff
N.J. Cook, Edouard Timsit, J. Colyn, Brady Chabot, Tong Liu, B. Wolfger, A. L. Schaefer

Bibliographic record

VenueJournal of Imaging · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsUniversity of AlbertaUniversity of CalgaryAgriculture and Agri-Food CanadaAgriculture Food and Rural Development
Fundersnot available
KeywordsThermographyResidual feed intakeConsistency (knowledge bases)ResidualThermalInfraredMeasure (data warehouse)Biological systemComputer scienceEnvironmental scienceAcousticsMaterials scienceMathematicsBiologyFeed conversion ratioOpticsPhysicsThermodynamicsBody weightArtificial intelligenceAlgorithmData miningEndocrinology

Abstract

fetched live from OpenAlex

Measurements of radiated thermal output are claimed to reflect the metabolic efficiency of mammals. This is important in food-producing animals because a measure of metabolic efficiency may translate to desirable characteristics, such as growth efficiency or residual feed intake, and permit the grouping of animals by metabolic characteristics that can be more precisely managed. This study addresses the question of whether radiated thermal parameters are characteristic of individual animals under normal and metabolically-challenging conditions. Consistency in radiated thermal output was demonstrated over a period of four weeks on condition that a sufficiently representative sample of measurements could be made on individual animals. The study provided evidence that infrared thermography could be used as an automated, rapid, and reliable tool for assessing thermoregulatory processes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.218
Teacher spread0.209 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of ImagingSame topicEffects of Environmental Stressors on LivestockFrench-language works237,207