Phenotypic and genetic correlations of beef replacement heifer feeding behaviour, feed intake and feed efficiency with cow performance and lifetime productivity
Bibliographic record
Abstract
Abstract Objectives were to quantify the phenotypic (rp) and genetic (rg) correlations between early‐life feeding behaviours, dry matter intake, and feed efficiency and measures of cow performance and lifetime productivity traits. Traits were measured on 1,145 crossbred replacement beef heifers and then on cows over parities one to four. Feeding event duration (FD) was phenotypically correlated with cow prebreeding body weight (PBWT; rp 0.29–0.45), cow prebreeding back fat thickness (PBBF; rp 0.35–0.49), progeny weaning weight (WW; rp 0.09–0.31) and progeny birthweight (BW; rp −0.06 to 0.17). Feeding event frequency (FF) was phenotypically correlated with PBBF (rp 0.16–0.30). Dry matter intake (DMI) was phenotypically correlated with PBWT (rp 0.16–0.20) and PBBF (rp −0.22 to −0.05). Feeding event duration was genetically correlated with PBWT (rg 0.38–0.41). Feeding event frequency was genetically correlated with PBWT (rg −0.43 to −0.39). Dry matter intake was genetically correlated with PBWT (rg −0.27 to 0.14). Days in herd (DIH) was phenotypically correlated with FD and DMI (rp = 0.12, 0.20, respectively). Lifetime productivity was phenotypically correlated with FD and FF (rg = 0.25, 0.22, respectively). Calving interval was phenotypically correlated with FD and FF (rp = −0.12, −0.14, respectively) and genetically correlated with FF (rg = −0.41). Due to moderate positive correlations with cow weight, caution would be required in selection to prevent an increase in mature cow size. Use of FF, FD, DMI and a measure of feed efficiency such as residual feed intake adjusted for back fat (RFIFAT) in a balanced selection index is recommended.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".