Impacts assessment of air emissions from point sources in Saskatchewan, Canada — A spatial analysis approach
Bibliographic record
Abstract
Air pollutants emanating from industrial point sources account for a large proportion of air pollution issues. Besides, emissions from these sources are technically controllable while other sources, for example, soil erosion, forest fires, and road travel, are subject to some unpredictable natural or economic factors. Therefore, most research efforts regarding air pollution control have concentrated on industrial point sources. In this study, an effective approach with the aid of spatial analysis is presented to evaluate the potential impacts of air pollutant emissions from point sources in Saskatchewan, Canada. Trend analyses are first carried out to demonstrate the temporal changes in the total number of and the spatial distribution of point sources from 1994 to 2008. Then, the IDW method is used to generate interpolation surfaces for main air pollutants emitted by industrial sources with the purpose of disclosing their emission patterns. Following that, 10 representative industrial facilities are screened out to estimate the impacts of PM2.5 on the surrounding residents, aiming to demonstrate the effectiveness of the proposed approach. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 304–313, 2015
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".