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Effects of an allostatic modulator on the behavior and blood indicators of young bulls after transport

2019· article· en· W2913742105 on OpenAlexaff
María Salud Rubio Lozano, Rubén Danilo Méndez Medina, Karla Reyes Mayorga, María Elena Rubio García, T.M. Ngapo, Karen F. Mancera, Francisco Aurelio Galindo Maldonado

Bibliographic record

VenueVeterinaria México OA · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAgriculture and Agri-Food Canada
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
KeywordsAllostatic loadAscorbic acidSodiumEndocrinologyCreatinineInternal medicineChemistryAggressionPotassiumMedicinePsychologyDevelopmental psychologyPhysiologyFood science

Abstract

fetched live from OpenAlex

An allostatic modulator (AM) composed of ascorbic acid, acetylsalicylic acid, sodium chloride and potassium chloride was dissolved in the drinking water of three groups of young bulls (n = 7) and administered to them for 7 d after 20.42 h of transport (Control (C) = 0 g/head/d, G2 = 30 g/head/d, G3 = 60 g/head/d). The effects of the AM on behavior and blood cellular and biochemical components were measured. G3 animals spent less time lying and more time standing than C animals (P = 0.001 and P = 0.02, respectively), indicating less fatigue. G3 bulls presented a lower frequency of agonistic interactions than those in the G2 group (P = 0.001), suggesting a possible effect on aggression that warrants further evaluation. The AM affected creatinine kinase (CK) activity in the G2 group compared to the C group (P = 0.04), suggesting an effect of AM components that has not been previously reported. Cortisol levels remained unaffected by AM supplementation (P = 0.55). These preliminary findings suggest an effect of supplementing cattle with an AM after long-haul transport.

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 score0.795
Threshold uncertainty score0.223

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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