Genetic Parameters for Feet and Leg Traits Evaluated in Different Environments
Bibliographic record
Abstract
The objective of this study was to test for genotype x environmental interaction (GXE) for feet and leg traits scored in different environments. Genetic correlations of seven feet and leg traits were estimated across different management systems: free versus tie stalls, slatted versus solid flooring, and intact versus trimmed hooves. Data were records from first-lactation Holstein cattle. Traits were claw uniformity, depth of heel, rear leg rear view, foot angle, bone quality, rear leg side view, and overall feet and legs. Different subsets of data were used for each comparison, resulting in 147,400; 53,550; and 145,160 records for housing, flooring, and hoof trimming management systems, respectively. Genetic parameters were estimated using REML and two-trait models in which for each animal a given trait was observed in one environment and missing in the other. Phenotypic scores were lower with tie stalls, slatted floors, and no trimming. Heritabilities tended to be greater in herds with tie stalls and slatted floors. Trimming had little effect on genetic parameters. The genetic correlations of feet and leg traits across pairs of management systems were > or = 0.85, except for rear legs, rear view. Therefore, effects of GXE were assumed to be of little importance and modification of genetic evaluation procedures on the basis of housing, flooring, and hoof conditions seems unnecessary.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".