Among farm variation in heifer BW gains
Bibliographic record
Abstract
BW of replacement heifers is rarely measured on commercial farms, making it difficult to evaluate the success of management practices related to calf growth. Our aims were to describe variability among commercial farms in Holstein heifer BW, determine how BW differences varied with management and propose a method of estimating calf growth based upon single measurement. Heart girth circumference was used to estimate BW of 576 heifers 48 to 70 weeks of age on 33 different farms (on average 11 ± 6 heifers/farm) in British Columbia, Canada. Regression analysis showed a linear relationship of BW with age (BW (kg)=116+5 × age (weeks)). Residuals from this regression were averaged across heifers within each farm to identify farms where heifers were heavier or lighter than would be predicted on the basis of their age; farm average residuals ranged from -54 to 72 kg. Farms with heifers showing the highest residual BW also had the highest rates of gain for pre-weaned calves. These results indicate that farms able to rear faster growing calves before weaning were also rearing faster growing heifers at breeding, and suggest that management of milk-fed calves is a particularly important component of replacement heifer management.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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, 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".