Applying the Grey Systems Theory to Assess Air Quality in La Oroya - Peru
Bibliographic record
Abstract
Air pollution is a problem in several mining and metallurgical operations, which can increase if the processing plants do not respect environmental standards.This work evaluates air quality with three monitoring stations in the years 1999, 2008 and 2014 in the city of La Oroya, near the Metallurgical Complex of the same name.The method used in this research was the grey clustering method, which is based on the grey systems theory.This technique allows working with data with a high degree of incertitude.An example would be the air quality analysis data.The results of this investigation reveal that in the year 1999 the air quality was extremely poor, while in 2008 it varied from poor to extremely poor; however, in 2014 the calculations show that the quality is good.These conclusions are obtained from the Ontario Ambient Quality Criteria (AAQC) and the Metropolitan Air Quality Index (IMECA).The results of this inquiry could motivate the competent authorities to carry out more studies to confirm that the air quality in La Oroya is good, since it was ranked as the fifth most polluted city.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".