An evaluation of corticotropin-releasing hormone and leptin SNPs relative to cattle behaviour
Bibliographic record
Abstract
Pugh, K. A., Stookey, J. M. and Buchanan, F. C. 2011. An evaluation of corticotropin-releasing hormone and leptin SNPs relative to cattle behaviour. Can. J. Anim. Sci. 91: 562–572. The purpose of this study was to identify associations between single nucleotide polymorphisms (SNPs) in two genes involved in the hypothalamic-pituitary-adrenal axis and growth, namely corticotropin-releasing hormone (CRH), and leptin (LEP), and measurements of temperament in beef cattle. Four hundred crossbred beef steers were evaluated upon entry into a beef facility using several different measurements of response to handling: subjective score (SS), strain gauge (SG), movement measurement device (MMD) and exit time (ET). The steers were genotyped at the CRH 22C>G, CRH 240C>G and LEP 73C>T SNPs by PCR-RFLP. The SNP genotypes and two-way interactions between LEP and each CRH SNP were analyzed as effects on the various temperament measurements. We found interactions between CRH 22C>G and LEP and CRH 240C>G and LEP with SG. Within this interaction there appears to be a positive effect of one CRH allele (C) within LEP TT animals while in LEP CC the other CRH allele (G) had a positive effect. These interactions, especially between CRH 22C>G and LEP, needs to be confirmed in other populations of beef cattle. It may be possible in the future to select for temperament alongside production goals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".