Associations between air emissions from sour gas processing plants and indices of cow retainment and survival in dairy herds in Alberta.
Bibliographic record
Abstract
This paper describes the results of an investigation into the effects of air emissions from sour gas processing plants on indices of retainment or survival of adult female dairy cattle on farms in Alberta; namely, the productive lifespan of individual animals, and annual herd-level risks for culling and mortality. Using a geographical information system, 2 dispersion models--1 simple and 1 complex--were used to assess historical exposures to sour gas emissions at 1382 dairy farm sites from 1985 through to 1994. Multivariable survival models, adjusting for the dependence of survival responses within a herd over time, as well as potential confounding variables, were utilized to determine associations between sour gas exposure estimates and the time from the first calving date to either death or culling of 150210 dairy cows. Generalized linear models were used to model the relationship between herd-level risks for culling and mortality and levels of sour gas exposure. No significant (P < 0.05) associations were found with the time to culling (n = 70052). However, both dispersion model exposure estimates were significantly associated (P < 0.05) with a decreased hazard for mortality; that is, in cases where cattle had died on-farm (n = 8743). There were no significant associations (P > 0.05) between herd culling risks and the 2 dispersion model exposure estimates. There was no measurable impact of plant emissions on the annual herd risk of mortality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".