Non-steady-state modeling of effects of timing and level of concentrate supplementation on ruminal pH and forage intake in high-producing, grazing ewes1
Bibliographic record
Abstract
A computer model was developed to predict responses of lactating ewes to concentrate supplementation, whether on pasture or stall-fed, given concentrate once per day or in multiple feedings, and suckling multiple lambs. The model considers effects of concentrate supplementation on organic acid production, saliva flow, ruminal pH, and forage intake. The user defines ewe BW, feed composition, and concentrate feeding times and amounts. The reference ewe has free access to forage and water. Upon consumption, forages and concentrates enter into lag pools for 2.0 and 0.24 h, respectively. Carbohydrates then enter ruminal pools of degradable fiber, undegradable fiber, or nonstructural carbohydrate, from which they are degraded or pass to the lower gut. Rapid dissociation of organic acids from carbohydrate fermentation and buffers from rumination are simulated to determine ruminal pH according to the Henderson-Hasselbach equation. The pH, in turn, affects fiber degradation rates. Forage intake continues during daylight hours until ruminal NDF exceeds 1.0% of BW, or organic acid concentration exceeds 130 mM. A circadian pattern of organic acid concentrations and pH of rumen contents with multiple concentrate feedings was simulated by the model with root mean square prediction error of 7.7 and 3.0 to 4.0% of the observed mean, respectively. However, ignoring fermentation of dietary protein may have caused an underestimation of organic acid production rates. The model predicted the increase in total DMI and the substitution effect on forage intake of increasing levels of concentrate supplementation. Simulations suggested that a single concentrate meal daily was best fed in the evening to minimize the substitution effect, and that there was no benefit in forage intake to feeding 2 kg/d concentrate in more than two meals per day.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".