Residual feed intake measured as replacement heifers is indicative of residual feed intake measured as mature cows
Bibliographic record
Abstract
This study aimed to quantify the relationship between residual feed intake (RFI) measured in 500 heifers and subsequently as mid-gestation cows at the Roy Berg Kinsella Research Station (KIN; n = 227) and Lacombe Research and Development Centre (LRDC; n = 273). Heifers were initially tested for RFI adjusted for end of test rib fat (RFIfat) at 8–12 months of age and then again as 3-year-old first-calf heifers at KIN and as 4–13-year-old cows at LRDC. Heifer RFIfat measured in drylot on a forage diet was associated ( R2 > 0.53; P < 0.001) with RFIfat measured again as older cows. Each 1 kg DM day−1 change in heifer RFIfat equaled 0.48 ± 0.10 and 0.75 ± 0.19 kg DM day−1 change in cow RFIfat for KIN and LRDC, respectively. Linear effects were also reported for RFIfat component traits, where DMI ( P < 0.001), ADG ( P < 0.060), mid-test metabolic weight ( P < 0.001), and end of test rib fat ( P < 0.001) measured as heifers were related when measured again as older cows. In addition, the linear effects of heifer RFIfat on cow RFIfat were constant across cow age groups from 4–10 years of age. These results show that selection for RFI in heifers will result in cows that are also more feed efficient.
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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".