Estimates of calf starter energy affected by consumption of nutrients. 1. Evaluation of models to predict changing digestion on energy content in calf starters
Bibliographic record
Abstract
Apparent total-tract digestibility data from 3 published studies with calves from 0 to 4 mo of age were used to evaluate National Research Council (2001) estimates of metabolizable energy (ME) in calf starters (CS). Calves (n = 83) or pens of calves (n = 24) were used in model development. In each study, 48 Holstein bull calves (2 to 3 d of age at initiation of each study) were fed varying amounts of milk replacer with CS and water for ad libitum consumption. Calf starters varied in nutrient content and form (pelleted, texturized, or mixed with 5% grass hay and fed as a total mixed ration). Apparent total-tract digestibility was measured at various ages from 3 to 16 wk. Feed and feces were collected from 20 calves per trial during 5-d collection periods during the first 56 d of each trial. In 2 studies, calves were grouped in pens (4 calves/pen) for a second 56-d measurement period. Fecal collections were repeated occasionally during the second period. Total-tract digestibilities (n = 207) of neutral detergent fiber, nonfiber carbohydrates, crude protein, and fat were used to calculate digestible energy (DE) and ME in CS using equations from the 2001 Dairy National Research Council. Three modeling approaches were constructed to evaluate changing digestion of nutrients, DE, and ME in CS, including linear mixed models, broken-line regression, and exponential models. Linear mixed models provided best model fit statistics for digestion of crude protein, ether extract, neutral detergent fiber, and ME. Exponential models were optimal for digestion of dry matter and nonfiber carbohydrates. Linear mixed models were selected for evaluation of effects of intake on changing nutrient digestion from CS and amount of DE and ME available at various ages.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".