PSIII-12 Genetic analysis of heat tolerance in Holsteins using test-day production records and satellite-based meteorological data
Bibliographic record
Abstract
Abstract Heat stress in dairy cattle is an existing issue in temperate regions that can cause reduced milk production, impaired fertility, and mortality. Genetic selection for heat tolerance using test-day production records and weather station data is a potential mitigation strategy. However, weather stations can have temporal data gaps and a low spatial resolution, which reduces the number of herds that can be incorporated into an analysis. The objectives for this study include: (1) compare satellite-based meteorological data from the NASA POWER database to weather station records in Ontario and Quebec, (2) evaluate the effects of heat stress on Canadian Holsteins, and (3) assess breeding value estimates for heat tolerance in the same population. Daily estimates of ambient temperature, dewpoint temperature, relative humidity, and wind speed from 481 weather stations in Ontario and Quebec were compared to the parameters estimated by the NASA POWER project using an ordinary least squares regression. The coordinates of herds in Ontario and Quebec were estimated using their addresses and Google Maps Geocoding. The best weather data for each herd location will be incorporated into two random regression animal models to analyze three test-day production traits: milk, fat, and protein yield. The first model will be used to estimate general and specific additive genetic merits over the thermal gradient. The second model will estimate the traditional additive genetic merit. In conclusion, this study explores the use of satellite estimated meteorological parameters in addition to or alternatively to weather station data in heat tolerance studies, quantifies the sensitivity of Canadian dairy cattle to heat stress, and evaluates if genetic selection for increased heat tolerance in Canadian dairy herds is possible.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".