PSV-18 The role of leptin in feed efficiency and behavior attributes of commercial beef heifers
Bibliographic record
Abstract
Abstract Leptin is a 16-kDa-peptide hormone product of the leptin gene (LEP) that is predominantly synthesized by adipose tissues and has known involvement in regulation of feed intake, energy expenditure, reproduction, and immune functions. The objective was to determine the association of leptin genotype (LEP c.73C >T), leptin diplotype (LEPD) and plasma leptin hormone (LEPH) concentration with growth, feed intake, feed efficiency, and behavior characteristics in developing beef heifers. A total of 336 commercial beef heifers were genotyped for the LEP c.73C >T marker. Four single nucleotide polymorphism markers including LEP c.73C >T, ARS-BFGL-NGS-59298 (intron 1), BovineHD0400026029 (upstream) and BovineHD0400026063 (downstream) of LEP were considered for haplotype analysis. Eleven LEPD were identified for this study using parsimony-based analyses. Circulating levels of LEPH were measured on 333 heifers prior to their first breeding season and used to assign low or high concentration group based on population median. Data were analyzed using the mixed procedure of SAS for repeated measures, fixed effects of ancestral breed group (n = 4), dam age category (n = 4), frame size group (n = 4), project cycle nested within birth year (n = 6), week of feed trial × year, LEP (n = 3) or LEPD (n = 11) or LEPH (n = 2), as well as week × LEPH (only for LEPH). There were no significant differences between LEP genotypes (P ≥ 0.11) or LEPD (P ≥ 0.33) for any of the studied traits. Heifers with low LEPH ate fewer meals per day (P = 0.02) and had more DMI per meal (P = 0.04) compared to those with high plasma leptin. Further research is needed to better understand plasma LEPH concentration and its role in feeding behavior attributes. However, circulatory LEPH before the breeding season may serve as a predictor for feeding behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".