ODOR IMPACT ASSESSMENTS BASED ON DOSE-RESPONSE RELATIONSHIPS AND SPATIAL ANALYSES OF POPULATION RESPONSE
Bibliographic record
Abstract
Owners of odor-emitting facilities currently lack effective strategies for assessing odorous impacts on communities. The most widely-used method for odor quantification is through estimates of odor concentration at impacted receptors in a community. This approach fails to account for the full range of dilutions over which an odor is experienced, the varied sensitivities of individuals in a population, and odor offensiveness. Therefore, dose-response relationships were developed to express the probability of response and degree of annoyance of a population as functions of odor concentration. Dispersion modeling can be used in conjunction with these relationships to calculate contours of probability of response and annoyance throughout the impacted community under many meteorological conditions. These contours serve as the basis for evaluating parameters that reflect various dimensions of odor impact on individual receptors and throughout the area of the impacted region. Spreadsheet software that was developed to aid in the calculation of these parameters is briefly presented.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".