A Phenotype Correlation Type between Individual Linear Type Traits Functional Score and 305 d Corrected Milk Yield of a Holstein Herd
Bibliographic record
Abstract
【Objective】Data of linear type trait scores and 305 d milk yield records of 40 healthy Holstein cows was analyzed in the study.【Method】Functional type traits were scored according to system recommended by the Canadian Holstein association,and SAS software was used to establish the regression equation between 305d milk yield and the measured linear functional type traits.【Result】Results of correlation analysis indicated that there was significant difference between 305 d milk yield and rump width,chest width,body depth,udder attachment and teat length(P0.01).A regression equation was established between 305d milk yield(Y) and chest width(X2),body depth(X3),rump width(X6),fore udder attachment(X9),rear udder height(X10),teat placement(X14),which was as follows: Y=-5529.945+59.456X2+3.024X3-3.069X6+70.173X9+19.834X10-8.892X14,R2=0.9231.【Conclusion】The milk yield of Holstein cattle could be predicted by the above six traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".