Evaluation of Mining Wastewater from the Shinca Creek Reused in Agriculture in a Rural Hamlet
Bibliographic record
Abstract
Abandoned mines and associated mine effluents, tailings ponds, and dumps pose a significant threat to human and environmental health and should be addressed as part of the environmental responsibilities of the mining industry.The objective of the study was to determine the extent to which the water of Quebrada Shinca (Peru) is contaminated with total metals and to outline possible applications in agriculture in the area.Water quality was monitored at three different times, using readings taken from seven environmental water monitoring points and three soil points, conducted in July, October 2022, and March 2023.The results are shown over the time intervals.The QISCA 03 (surface water) water flow is predicted to increase by 1603.04% from the reading taken in July 2022 to March 2023 and its values were below the Environmental Quality Standards for water (EQS), the QISCA 04 (mine effluent) flow reached (133.05%) and the values were (As=0.78,Cd=0.16,Cu=0.80,Zn=42.67)mg/l and for QISCA 07 (irrigation water) the flow increased by 92.42% and the values were (Cd=0.05,Mn=14.10,Pb=0.12 and Zn=13.94)mg/l in the same period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".