Effect of beef heifer development system on average daily gain, reproduction, and adaptation to corn residue during first pregnancy1
Bibliographic record
Abstract
Postweaning heifer development systems were evaluated at 2 locations in a 4-yr study for their effect on performance and subsequent adaptation to grazing corn residue as a pregnant heifer. In Exp. 1, heifers were blocked by BW and randomly assigned to graze winter range (WR) or graze winter range and corn residue (CR). In Exp. 2, heifers were assigned to graze winter range and corn residue (CR) or graze winter range and placed in a drylot (DL). Artificial insemination and natural mating were used at breeding on the basis of location. In Exp. 1, heifers developed on corn residue tended (P = 0.11) to have reduced ADG compared with WR heifers. Subsequently, BW at the end of the 82-d corn residue grazing period tended (P = 0.09) to be lower for CR compared with WR heifers. However, the proportion of heifers attaining puberty before the breeding season and pregnancy rates were similar (P ≥ 0.29) for CR and WR heifers. Developing heifers on winter range tended (P = 0.09) to reduce heifer development costs $36/pregnant heifer compared with CR heifers. In Exp. 2, DL heifers had greater (P < 0.01) overall ADG during development compared with CR heifers, resulting in greater (P < 0.01) prebreeding BW for DL heifers compared with CR heifers (355 vs. 322 ± 9 kg). At pregnancy diagnosis BW remained greater (P = 0.02) for DL compared with CR heifers (423 vs. 406 ± 7 kg). Corn-residue-developed heifers had increased (P = 0.03) AI conception rates compared with DL heifers (78% vs. 67% ± 6%). However, there was no difference (P ≥ 0.21) in percent pubertal before the breeding season or final pregnancy rates for CR and DL heifers. Developing heifers on corn residue reduced (P = 0.02) heifer development costs $38/pregnant heifer compared with DL-developed heifers. A subset of pregnant heifers from both experiments grazed corn residue fields in late gestation. As pregnant heifers grazing corn residue, WR heifers (Exp. 1) tended to have reduced ADG compared with CR heifers (0.34 vs. 0.43 ± 0.08 kg/d, P = 0.07). Furthermore, in Exp. 2 CR heifers had greater (0.41 vs. 0.30 ± 0.22 kg/d) ADG grazing corn residue as pregnant heifers compared with DL-developed heifers. Calving date, dystocia score, and calf birth BW were similar (P ≥ 0.15) between development systems in both experiments. There appears to be a potential learned grazing behavior for heifers developed on corn residue allowing them to better adapt to grazing corn residue as pregnant heifers compared with WR and DL heifers.
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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 | 0.002 | 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) | 0.000 | 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".