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Record W2973253447 · doi:10.1139/cjas-2018-0017

Dietary fatty acid content and thickness of plantar pads in gilts

2019· article· en· W2973253447 on OpenAlexvenueno aff
Juan Grandía, Luís V. Monteagudo, Paloma Sánchez-Abad, María Teresa Tejedor

Bibliographic record

VenueCanadian Journal of Animal Science · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsOleic acidPalmitic acidAnimal scienceFatty acidChemistrySignificant differenceMedicineFood scienceInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

The objective of this study was to test a diet enriched in the most abundant components of foot fat pads (oleic and palmitic acid) to increase its thickness in gilts. We evaluated the effects of two oleic and palmitic acid dietary concentrations (control and test) and three treatment durations (35, 45, and 65 d) on 116 gilts (Landrace × Large White), all 180-d-old and slaughtered at the end of the study. Both test and control diets contained 5.9% total fat. The control diet contained 0.9% oleic acid and 0.6% palmitic acid; the test diet contained 1.9% and 1.2%, respectively. Body weight (BW), backfat (BF), lateral, and medial plantar pad thickness from the left rear leg were measured. No significant differences were detected for BW or BF between the test and control groups. The lateral pad was always thicker than the medial one (P < 0.001). No significant difference for plantar pad thickness was detected for the 35 d treatment. For the other treatments, thickness increased with respect to the control group (P < 0.01); the percentage of increase ranged from 20.8% (lateral side, 45 d treatment) to 37.8% (lateral side, 65 d treatment). Its effects on foot health must still be demonstrated.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.143
GPT teacher head0.351
Teacher spread0.208 · 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
Published2019
Admission routes1
Has abstractyes

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