MAPPING DAIRY COW HEAT STRESS IN SOUTHERN ONTARIO—A COMMON GEOGRAPHIC PATTERN FROM 2010–2012
Bibliographic record
Abstract
In Southern Ontario, climate change has resulted in an increased occurrence of heat waves, causing heat stress among humans and livestock, with potentially fatal consequences. Heat waves are defined as three consecutive days of temperatures greater than or equal to 32°C. Maps visualizing the distribution of heat stress can provide information about related health risks and insight for control strategies. Weather data were collected from weather stations throughout Southern Ontario for dry bulb temperature and dew point temperature. The Dairy Cow Heat Stress Index (HSI) was estimated by averaging the first three days for three heat waves, during 2010, 2011 and 2012. Geostatistical kriging was used to map three-day averages of maximum heat stress over periods involving a heat wave and control periods three weeks prior to and following heat waves. Average HSI for each period across Southern Ontario ranged from 55 to 78 during control periods, and from 65 to 84 during heat waves, surpassing levels where mortality is known to increase substantially. Heat stress followed a consistent geographic pattern with the most affected areas in the southern region of the study area, surrounding major metropolitan areas. These HSI maps indicate areas which are less optimal for dairy farming within the study boundary. Thus, some areas currently used for dairy farming and at high-risk for heat stress mortality may require heat abatement strategies to sustain dairy cow production as heat waves become more frequent and intense due to climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".