78 Effects of strategic supplementation of low quality diets and previously determined residual feed intake rank on performance in gestating beef cattle.
Bibliographic record
Abstract
A two-year wintering beef cow study was conducted evaluating effects of strategic supplementation of straw-based diets with an energy-protein supplement fed over the last 75 days of gestation on cow and pre-weaning calf performance. Residual feed intake (RFI) ranks from the 2015 gestation period (RFI2015) were compared to RFI determined in 2016 and 2017 using intraclass correlations and Spearman correlation coefficients. Gestating crossbred beef cows (n = 116, 141 ± 18 days in gestation, 672.6 ± 99.2 kg BW) were allocated to one of five nutritional regimens (NR): 1) 70% haylage, 30% millet straw on a dry matter basis (DMB) fed ad libitum (HAYL), 2) straw fed ad libitum along with haylage at 0.8% DMB of cow BW (MSHAYL), 3) MSHAYL along with energy-protein supplement once/week, 4) twice/week, or 5) three times/week. Supplement contained 54.6% corn gluten meal and 23.4% SBM, and 21.0% calcium propionate, fed at 0.32 g/kg BW per day (DMB). Cows were evaluated during 22015 values were ranked into quartiles; 12015 rankings. HAYL cows had greater (P < 0.001) gestation DMI, ADG, and fat deposition. No effects (P > 0.05) for supplementation or supplementation frequency were found except that supplementation increased (P = 0.016) DMI. RFI2015 rank was not repeatable (R = 0.06, P > 0.48), nor correlated to RFI rank measures found during 2016 (r = -0.03, P > 0.76) or 2017 (r = 0.23, P < 0.06). Both NR and year affected (P < 0.001) reranking between years. Overall, RFI ranks measured during 2015 were not repeatable between years and were affected by NR. Wintering cow performance was related to NR.
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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 | 0.000 |
| 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".