Validation of an air dispersion model for odour impact assessment
Bibliographic record
Abstract
Odorous emissions can result in physiological and psychological discomfort when released and subsequently perceived by people in the neighbouring community.Regulatory agencies, as well as members of the industrial and agricultural sectors, are obliged to develop methods for mitigating or preventing odorous impacts on communities.It has been proposed that combined application of the Industrial Source Complex-Short Term3 (ISCST3) dispersion model and the Odour Impact Model (OIM) can provide an improved basis for predicting odorous impacts.The objective of this investigation was to validate the use of ISCST3 and the OEM to predict the impact of emissions from a hog farm in rural Quebec in terms of probability of response and degree of annoyance.This was accomplished by predicting the impact through modelling for comparison with on-site field measurements that were conducted on three different occasions.The ISCST3 dispersion model was used to predict odour concentrations in the region in the immediate vicinity of the farm.Subsequently, the predicted concentrations were used in combination with the dose-response curves of the OIM to predict the probability of response and annoyance that would be experienced in the region surrounding the hog farm.When compared to field measurements, it was concluded that the model resulted in reasonably accurate predictions provided that the predicted one-hour time-averaged concentrations from the dispersion model were first transformed to one-minute timeaveraged values.Overall, once this transformation was made, there was a tendency to slightly under predict the probability of response and to slightly over predict the degree of annoyance.The difference in these tendencies may result from differences in the ways that odour are characterized in the laboratory as compared to how they are experienced in the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".