Determining tolerance to odour annoyance in communities near hog operations.
Bibliographic record
Abstract
Determining tolerance to odour annoyance in communities near hog operations.Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 56: 6.1-6.11.Quantifying the impact of odour from livestock facilities has been a challenge to researchers and environmental regulators.Physical and sensory odour attributes are commonly used to define the odour impact.However, people's responses (tolerances) to odour may be affected by many other nonphysical factors.The objective of this study was to determine odour impact in broader scopes and domains by considering such factors as quality of life and community values.Face-to-face survey interviews were conducted in five communities to assess the residents' responses to odour from hog operations.Based on the interview data, odour annoyance to residents was quantified and their tolerance to odour was determined.It was found that the annoyance at hog odour was greater than at other odour sources.Some residents (13%) would not tolerate hog odour even if they indicated that they were not annoyed by odour.On the opposite side, about 13% of residents would tolerate hog odour even if they indicated the highest annoyance level (10).Residents living in the rural areas were more tolerant of hog odour if the odour sources (hog operations) had previously been in existence.Very few (<1%) residents identified hog odour as the cause of headaches, waking up at night, and nausea.Five percent of residents indicated that their outdoor enjoyment was disrupted by odour, but only rarely or sometimes.Keywords: hog,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".