First calving performance and physiological profiles of 2-year-old beef heifers according to their pre-breeding growth
Bibliographic record
Abstract
This experiment was designed to assess the effects of different feeding strategies before first breeding at 15 mo on performance and physiological parameters of beef heifers during their gestation and first lactation. During preweaning (PRE, 0–6 mo) and postweaning periods (POST, 6–15 mo), 25 Parda de Montaña heifers were fed to achieve gains of 1.0 kg d−1 (HI) or 0.7 kg d−1 (LO) in a 2 × 2 factorial design (HI–HI, HI–LO, LO–HI, and LO–LO). Although calf birth weights did not differ, heifers from LO–LO treatment had the greatest calving assistance (80%), probably because they were lighter than the rest (436 ± 39 kg body weight, P < 0.01) and had the smallest external pelvic area (19.5 ± 2.0 dm2, P < 0.01). Milk production and calf gains during lactation were similar among treatments. Cows from POST–HI treatment tended to be cyclic earlier than POST–LO ones (82 ± 8.4 and 106 ± 9.4 d post partum, respectively; P = 0.06). Feeding managements did not influence metabolic (glucose, cholesterol, nonesterified fatty acid, β-hydroxybutyrate, and urea) or endocrine (insulin-like growth factor I and leptin) profiles of heifers. Continued low feeding levels before breeding heifers at 15 mo are not recommended, because they may hinder primiparous calving performance.
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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 | 0.001 |
| 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".