Evaluation of the National Research Council (NRC) nutrient requirements for beef cattle: Predicting feedlot performance
Bibliographic record
Abstract
The accuracy of predicted CowBytes ® versus actual dry matter intake (DMI) and average daily gain (ADG) of 407 Hereford × Angus and Charolais × Maine Anjou (445.6 ± 36 kg) feeder cattle using digestable enery acid detergent fiber (DE) estimated from the (ADF) content [Laboratory analysis method (LAB)] and from values determined in vivo (INVIVO method) was examined. The diet consisted of a 73.3% concentrate diet, 22.0% barley silage, 1.6% molasses, and 3.1% feedlot supplement fed ad libitum (as-fed basis). The calculated DE values of the feed were used to predict the metabolizable energy (ME), net energy of maintenance (NE m ), and net energy of gain (NE g ) of the diet. These energy values were then used in CowBytes ® to predict dry matter intake (DMI), ADG, and days on feed (DOF) necessary to meet targeted quality grade of AA and weights of 522 and 568 kg for the heifers and steers, respectively. There was no effect of gender and prediction method interaction (P > 0.10) on any of the variables measured. There were no (P > 0.05) differences in predicted DMI by either the INVIVO or LAB method but both methods underestimated DMI actually consumed by the cattle by 6.8 and 4.9% (P = 0.007), respectively. Indeed, regression values from these predictive methods and actual DMI were (P < 0.05) different from the one-to-one relationship expected by definition. In spite of the higher actual DMI, the actual ADG of the cattle was 14 and 11% (P = 0.0004) lower than was predicted by either the INVIVO or LAB methods. A possible reason for the lower ADG could be an overestimation of DE of the diet. Thus, if available, users of CowBytes ® should use actual DMI from their experience in ration formulation. In addition, the effects of environmental temperature on digestibility of diets should be taken into consideration when using the DE of the diet as determined from in vivo digestibility trials or calculated from chemical analyses in determining the DMI of feedlot cattle. Key words: Beef cattle, performance, CowBytes ® , National Research Council
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".