Fuzzy Logic Modelling Of Air Pollution During Chinook Winds
Bibliographic record
Abstract
The occurrence of Foehn winds in the city of Calgary is due to the nearby proximity of the Canadian Rocky Mountains. The Foehn winds bring warm weather during the winter months and are described locally as Chinooks, a Blackfoot Indian term meaning snow eaters. Despite the increased wind speed during a Chinook, air pollution levels increase. A novel approach to modeling air pollution during a Chinook was developed using fuzzy logic. The Fuzzy c-Means method for system classification was used to identify the Foehn wind. As well, Modified Learning from Examples was used to predict surface ozone concentration. The identification of the occurrence of the Chinook wind was based on changes in humidity, and temperature increase. Key factors in air pollution for Calgary are ventiIation components such as wind speed. The new hzzy logic model developed in this work predicts the ozone concentration based on hourly wind speed and wind direction. The new model shows good agreement with the data and captures the trends in ozone concentration during Foehn wind conditions. Furthermore, this work shows that hzzy logic can be a powerful tool in air pollution modeling.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".