Applying the Grey Systems Theory to Assess Air Quality in La Oroya - Peru
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 LaOroya is good, since it was ranked as the fifth most polluted city.
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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 | 0.045 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.008 |
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 it