A Multiple Trait Selection Index Including Feed Efficiency
Bibliographic record
Abstract
This study was conducted to develop a multiple trait index including residual feed intake with the objective to improve net feedlot revenue in market progeny of tested bulls. The selection objective was defined as H= v 1 E 1 + v 2 E 2 + v 3 E 3 , where aggregate genetic merit (H) was a linear function of daily DMI (E 1 , kg/d), ADG (E 2 , kg/d), and slaughter BW (E 3 , kg) of progeny. Regression of steer (n = 426) net revenue on traits in the objective yielded the vector of economic weights ( v ) with elements v 1 = $−21.49, v 2 = $183.73, and v 3 = $0.27. The selection criterion was defined as I=b 1 X 1 + b 2 X 2 +b 3 X 3 , where index value (I) was a linear function of residual feed intake (X 1 , kg/d), ADG (X 2 , kg/d), and adjusted 365-d BW (X 3 , kg) phenotypes of tested bulls. Residual feed intake was defined as the difference between actual DMI (kg/d) and that predicted by phenotypic regression (R 2 = 0.69, residual SD=0.58 kg/d) of daily DMI on ADG, metabolic mid-test BW, and on-test gain in ultrasound subcutaneous fat depth and longissimus area in Angus bulls (n = 99). The matrix of genetic covariances of criterion traits with objective traits ( G ) was estimated from recent literature and the phenotypic matrix of (co)variances among criterion traits ( P ) was estimated from Angus bulls with test data. Criterion weights were obtained from the solution to b = P 1 Gv with elements b 1 = −10.12, b 2 = 24.79, and b 3 = −0.09. Index values ofbulls adjusted to a mean of 100 (SD = 7.81) ranged from 80.1 to 115.7. Bull ADG, residual feed intake, and 365-d BW accounted for 38, 48, and 14% of the variance in index values, respectively. Phenotypic correlation estimates (P < 0.001) for index values with bull daily DMI, ADG, and residual feed intake were −0.22, 0.53, and −0.74, respectively. Index value tended (P < 0.13) to have a lesser but favorable association with scrotal circumference. Bulls with greater index values, therefore, consumed less DM, had greater ADG, and were more efficient; however, index value was not associated (P > 0.89) with 365-dBW.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".