The Impact of Industrial Odors on the Subjective Well-Being of Communities in Colorado
Bibliographic record
Abstract
Odor pollution was identified as a top priority of the community of North Denver. Previous studies that investigated the impact of air pollution in North Denver focused on adverse health effects, rather than mental well-being. This study assessed the impact of odors from industrial sources on the subjective well-being (SWB) of North Denver residents, and of four similar communities in Colorado for comparison. An online survey was sent to participants from Greeley, Fort Collins, Fort Lupton, North Denver, and Pueblo, asking questions about SWB and odors in their areas (n = 351). The evaluation of SWB was performed using a novel approach that appraises three aspects of SWB. This approach of evaluating SWB has not been used in odor exposure studies. A proportional odds logistic regression model was used to estimate nine measures of SWB. The results showed that participants who reported that the air is very fresh or the odor is highly acceptable had higher levels of SWB. This association suggests that residents who live in areas exposed to strong industrial odors had lower levels of SWB. A subset of participants in this study took the survey four times in one year. Longitudinal analysis showed that evaluative satisfaction was slightly associated with seasonality. Both satisfaction with how life turned out and satisfaction with standards of living slightly increased during the fourth quarter of the year. The study also found that four of the nine measures can be used to represent SWB in future studies. Two of those measures were evaluative SWB, and the other two were positive hedonic SWB measures. A comparison between the five communities showed that well-being levels in North Denver and Greeley were not significantly different than those in Fort Collins or Fort Lupton. The comparison, however, showed that Pueblo had the lowest levels of well-being among all communities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".