Effects of nutritional management regimen and residual feed intake (RFI) classification on RFI reranking and feeding behaviour for finishing beef steers
Bibliographic record
Abstract
Over 2 years, 207 steers were fed an alfalfa/corn silage diet to determine growing phase (GP) residual feed intake (RFI) classification (low, medium, and high) for individual cattle. Steers were then allocated to two finishing management regimens (MR): MR 1 cattle gradually adjusted to an 84.7% concentrate (dry matter (DM) basis) diet; MR 2 included backgrounding on pasture before finishing using the same diet as MR 1 cattle. Treatment differences in growth performance were examined using GP and finishing phase (FP) RFI classifications. Based on GP RFI classification, FP average daily gain was greater in MR 2 cattle (P = 0.01) with no RFI classification differences for most FP performance traits. However, low-RFI steers had lower FP dry matter intake (DMI) and greater G:F (P ≤ 0.05) than high-RFI steers based on FP RFI classification. Low-RFI steers had fewer visits to the feeder with a lower eating rate than high-RFI steers in both production phases (P ≤ 0.05). Extensive RFI reranking occurred between production phases with 64.3% of steers changing RFI classification. Given extensive reranking in the present study, RFI classification was poorly repeatable between growing and finishing production phases when diverse diets are fed and does not accurately predict feed efficiency.
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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 | 0.001 |
| 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) | 0.001 | 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".