ODOR IMPACT ASSESSMENTS BASED ON DOSE-RESPONSE RELATIONSHIPS AND SPATIAL ANALYSES OF POPULATION RESPONSE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it