Evaluation of the 1996 NRC beef model under western Canadian environmental conditions.
Bibliographic record
Abstract
Two feedlot trials were conducted to evaluate the 1996 NRC beef model under western Canadian conditions. In the first trial, 144 Charolais- (304.6 +/- 16.3 kg) and 144 Hereford- (295.1 +/- 20.8 kg) cross steers were used, whereas the second trial used 88 Angus- (289.7 +/- 15.0 kg), 88 Charolais- (299.8 +/- 17.9 kg), and 88 Hereford- (291.1 +/- 20.9 kg) cross steers. Diets were based on barley silage, rolled barley grain, canola meal, and cereal straw and were analyzed according to the 1996 NRC methodologies. Animal performance and environmental data were collected for 24 pens of steers per trial for the backgrounding and finishing periods. Levels 1 and 2 of the 1996 NRC model were used to generate predictions of DMI and ADG for each pen. Results showed that actual finishing DMI was accurately predicted for Trial 1 and for the combined trials but not for Trial 2. Predicted ADG was lower (P < 0.05) than actual ADG for all feeding periods except Level 1 of the Trial 1 finishing period. All ADG residuals were significant (P < 0.05), indicating inaccurate prediction of ADG in all feeding periods. The 1996 NRC model consistently predicted that protein was not limiting gain. Further investigations and model refinement regarding animal energy requirements under cold weather conditions and effects of limit feeding are required to increase the accuracy of the 1996 NRC model in predicting animal performance.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".