Agricultural odours: 25 years of reducing complaints about barns and manure storages using the minimum distance separation formulae
Bibliographic record
Abstract
Ontario in Canada has a diverse livestock and poultry industry. Two million of Ontario's eleven million residents live in rural areas, but only 5% live on livestock and poultry farms, being outnumbered by their rural, non-livestock neighbours by 20:1. The increasing size, complexity, specialisation and concentration of livestock and poultry farms coupled with rural neighbours who have little or no family or business connection to them has resulted in an escalation in the number of odour complaints about barn and manure storage locations. Ontario-developed Minimum Distance Separation I and II formulae have helped site over 100,000 non-compatible uses, such as severed lots, away from livestock and poultry facilities, and similarly sited over 20,000 barns. However, they are under review because of the need to reflect the current and anticipated state of the livestock and poultry industry, the changing needs of the rural community, and to make it easier to apply for the growing number of municipal staff with little knowledge of the agricultural industry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".