Industrial Odor Source Identification Based on Wind Direction and Social Participation
Bibliographic record
Abstract
Industrial odors have been a major concern in many communities in Colorado (USA). Odor source identification is important for any mitigation strategy. The aim of this work was to identify odor sources using wind direction and odor data collected by social participation. For more than one year residents reported time, date, location and description of the odor occurrence by means of a smartphone technology. The odor spatial distribution and wind roses generated from local stations were used to identify odor sources. The majority of odor reports happened in North Denver (57%) and Greeley (33%). North Denver analysis showed that a single facility that manufactures pet food was responsible for the pet food odor (the most reported odor, 81 reports). Dead animal and sewage odors were associated with a North Denver meat and grease recycling facility, and the Metro Wastewater treatment plant, respectively. Roofing tar odor was probably associated with a facility that treats crossties and utility poles with creosote. Another odor that was often described as a refinery odor was less likely to be associated with the Denver oil refinery and more likely to be associated with one of the four facilities in the northwest of Globeville that uses asphalt and creosote materials. In Greeley, most reports (133 reports) happened in LaSalle, a small town in the southern part of Greeley. All reports from LaSalle described one offensive odor that was produced by a biogas facility east of LaSalle. The feasibility of odor source identification using wind direction and social participation was demonstrated. A regional cooperation to reduce odor problems in North Denver is highly recommended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".