Growth modeling of dairy heifers in Québec based on random regression
Bibliographic record
Abstract
Cue, R. I., Pietersma, D., Lefebvre, D., Lacroix, R., Wade, K., Pellerin, D., de Passillé, A-M. and Rushen, J. 2012. Growth modeling of dairy heifers in Québec based on random regression. Can. J. Anim. Sci. 92: 33-47. A total of 144 006 weight (calculated from tape girth measurements) and height data records from Québec dairy heifers were analyzed using random regression to estimate growth curve parameters of Ayrshires, Brown Swiss and Holstein animals to permit prediction of individual heifer growth from 0 to 32 mo. There were, on average, 5.15 records per heifer (minimum 3 records, maximum 25 records). The body weight data were analyzed using linear and quadratic fixed and random regressions, with a power-of-the-mean (POM) function to model the residual variance. The POM was 1.2 for Holstein and Ayrshire and slightly less than 1 for Brown Swiss. Estimated body weight at 24 mo was 507, 564, 624 kg, for Ayrshires, Brown Swiss and Holstein, respectively. The height data were analyzed with a Brody, monomolecular non-linear growth curve model. Mature height was estimated to be 148 cm in both Holstein and Ayrshires, and 150 cm in Brown Swiss. Random regression models were shown to be able to predict individual growth, and can be incorporated in decision-support tools to help producers reducing the average age at first calving.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".