Setting Acceptable Odor Criteria Using Steady-state and Variable Weather Data
Bibliographic record
Abstract
Odor travel distances predicted by air dispersion models, at which odor dilutes to a certain odor concentration, e.g. the chosen acceptable odor concentration, are different using steadystate and variable weather conditions, therefore, the acceptable odor concentration criteria should be different using these two types of weather data. The objective of this study was to determine the odor criteria that result in the same setback distance under these two types of weather conditions. Using CALPUFF model, the odor dispersion from a typical swine farm in Saskatchewan, Canada was modeled using both steady-state and annual hourly variable weather conditions. The model predicted concentrations under steady-state conditions and the annual occurrence frequencies of these weather conditions, and the model predicted concentrations and the exceeding frequencies under variable (hourly) weather conditions are compared and the equivalent combination of odor concentrations and frequencies under these two types of weather conditions are identified in order to achieve the same odor travel distances. These equivalent odor concentrations and frequencies under these two types of weather conditions may be used as acceptable odor criteria to determine setback distances from livestock farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".