Calidad del agua en la cuenca del Río Rímac - Sector de San Mateo, afectado por las actividades mineras
Bibliographic record
Abstract
--- The thesis of investigation approaches the effects that the water quality of Rimac river has presented opposite to the development of the mining activity in San Mateo's district of Huanchor located in Huarochirí's province in Lima. \nThe area of study is a zone where the mining polymetallic activity has developed from many decades behind approximately from the 30s, epoch in which there had not the current requirements of the environmental legal regulation and for such motive we have nowadays catalogued in the zone 21 environmental mining liabilities between bocaminas, relaveras and infrastructures seated on the banks of the Rimac water and of it’s principal tributaries like Blanco and Aruri rivers which nowadays are important sources of lixiviados to Rímac river water , due to the fact that they aren`t being handled by the private company and for the State. \nThe quality water investigation has been developed in a series of time of ten years taking as bosses of analysis to the metallic ions; which have had a comparative analysis with the national and international legal environmental regulations such as the Standards of the World Health Organization, the Standards of Canada for Water of Irrigation, the General Law of Waters and the National Standards of Quality of the Water (ECAS) for the Category III passes by the Supreme Decree N° 002-2008-MINAM, being the above mentioned the legal environmental decisive modal for the analysis of the quality of the water of the year 2008, since they constitute the ideal values that assure the quality of the water superficial resources of the country. \nWe obtained fron the analysis that the Cadmium, Lead, Manganese, Arsenic and Iron were the elements that had to receive a corrective treatment since their concentrations in Rimac water were bigger than the established in the standards of water quality.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 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".