DM intake by feedlot beef cattle: factors that impact intake patterns
Bibliographic record
Abstract
DM intake (DMI) for individual pens of cattle is recorded daily or averaged across each week by most commercial feedlots as an index of performance. Numerous factors impact DMI by feedlot cattle. Some are available at the start of the feedlot period (initial BW, sex), and others become available early in the feeding period (daily DMI during adaptation) or more continuously (daily DMI from the previous week). To evaluate the relative impact of these factors on daily DMI during individual weeks within the feedlot period, we employed a dataset compiled from 2009 to 2014 from one commercial feedlot, including 4 132 pens (485 458 cattle), which were split into two fractions: 80% were used to calculate DMI regressions on these factors to develop a prediction equation for mean DMI for each week of the feeding period, and 20% were reserved to test the adequacy of these prediction equations. Correlations were used to determine the relationship between all available variables with observed DMI. These variables were then included in the generalized least squares regression models. A veracity test of the model was performed against the reserved data. Daily DMI from previous week was the factor most highly correlated with daily DMI (P < 0.10) during from week 6 to week 31, accounting for approximately 70% of the variation, followed by mean daily DMI during adaptation period (weeks 1–4), including in the prediction model from weeks 5 to 12. Initial shrunk BW (ISBW) was the third most correlated factor, which was included in prediction equations from week 5 to week 20. Sex entered the prediction model only after week 8. Daily DMI for each test week within the feeding period was predicted closely (r2 = 0.98) by these four factors (RMSE = 0.155 kg). In conclusion, the mean daily DMI during each week of the finishing period for a pen of cattle could be predicted closely based on mean daily DMI intake during the previous week plus other variables available early in a feedlot period (daily DMI during adaptation period, ISBW and sex).
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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 | 0.000 | 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) | 0.003 | 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".