Genetic analysis of body condition score in Canadian Holsteins
Bibliographic record
Abstract
This thesis is preliminary research working toward a genetic evaluation of body condition score (BCS) in Canada using a random regression animal model, which would allow each animal to possess, and be ranked according to, a unique BCS lactation curve. Valacta, a milk recording agency based out of Que?bec, Canada, has been collecting several BCS records per cow since 2001. Using a multiple-lactation random regression animal model, it was determined that many of the same genes control BCS in each of the first three parities. Thus, collection of first-lactation BCS records would be sufficient for genetic evaluation. Additionally, this thesis investigated the change in BCS's relationship with various milk production traits over the lactation with a multiple-trait random regression animal model. Early lactation was when the genetic relationships between BCS and milk production traits were most favorable. Hence, early lactation BCS shows potential for selection. Because Valacta's BCS data was only available for Que?bec herds, other traits that were potentially strongly genetically correlated with Valacta's BCS, that were recorded nation-wide, were investigated. Holstein Canada collects records for type traits once at classification. Although Holstein Canada collects BCS, only 1 record is available per cow, such that a genetic evaluation of each animal's BCS curve would not be possible. Holstein Canada's BCS, angularity, and chest width were moderately to strongly genetically correlated with Valacta's BCS. Using all 4 traits in conjunction will allow for a genetic evaluation of each animal's unique BCS curve for Holsteins across Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".