Relationship between residual feed intake classification as a heifer and lifetime productivity of beef cattle
Bibliographic record
Abstract
The effect of residual feed intake adjusted for backfat thickness (RFIfat) on heifer pregnancy rate and subsequent lifetime productivity was examined in 867 beef females that were ranked as low, medium, or high RFIfat. Age at first calving, weaning weight of first calf, and most probable producing ability for birth weight (MPPAbw) and weaning weight (MPPAww) were calculated to assess first parity heifer productivity. The effect of heifer RFI adjusted for backfat (RFIfat; n = 532) on subsequent lifetime cow productivity (n = 415) was calculated based on kg of calf weaned per cow bred per year. A total lifetime productivity measure (n = 218) were also calculated as total calf weaning weight (kg) output per cow culled. RFI rank had no significant effect on pregnancy rate, when adjusted for season and site differences (P = 0.33). No significant correlations (P < 0.05) were observed between MPPAww and RFI, RFIfat, RFI adjusted for backfat and feeding event frequency (RFIfat & activity), or age at first calving. A negative trend (P < 0.10) between RFI, RFIfat, and MPPAbw calculated from first parity pregnancy rate and production traits was no longer apparent when adjusted for RFIfat & activity. These results suggest that selection for low RFI replacement heifers has no impact on their first parity pregnancy rate and productivity or on subsequent cow productivity.
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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 | 0.003 |
| 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".