Genetic Relationships of Fertility Disorders with Reproductive Traits in Canadian Holsteins
Bibliographic record
Abstract
Data. Health data recorded by dairy producers, as well as calving and fertility records were obtained from the Canadian Dairy Network (Guelph, Ontario). Health data on incidence of RP, MET and CYST were recorded by producers on a voluntary basis according to the disease definitions described by Kelton et al. (1998). A minimum disease frequency (reported cases per herd and year) of 1% was applied for RP, MET and CYST to ensure continuous data recording within individual herds. Data editing was applied separately for each disease, because not all herds record all fertility disorders (Neuenschwander, 2010; Koeck et al., 2012). Fertility disorders were defined as binary traits (0 = no case, 1 = at least one case) based on whether or not the cow had at least one disease case recorded within the first 14 d after calving for RP, within 150 d after calving for MET and within 305 d after calving for CYST. The calving traits were gestation length (GL) (in days), calving difficulty (CD; 1=unassisted, 2=easy pull, 3=hard pull, and 4=surgery), stillbirth (SB; 0=born alive, 1=dead at birth or within 24 h) and calf size (CZ; 1=small, 2=average, 3=large). Fertility traits were days from calving to first service (CTFS), 56-day non-return rate (NRR), number of services (NS) and days from first service to conception (FSTC). NRR was defined as a binary trait (0=return, 1=non-return), based on whether or not the cow had a second insemination within 56 d after the first insemination. Only first lactation Holstein cows with an age at first calving between 19 and 43 months were considered. A summary of statistics of the analyzed data set is given in Table 1. The sire pedigree file was generated by tracing the pedigrees of sires and maternal grandsires back as far as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".