Applying Grey Clustering and Shannon’s Entropy to Assess Sediment Quality from a Watershed
Bibliographic record
Abstract
The evaluation of the quality of sediments is a complex issue in the Peruvian reality, mainly because there is no sampling protocol or norm for comparison, which leads to the assessment of sediments without a comprehensive analysis of their quality. In the present study, the quality of the sediments in the upper basin of the Huarmey river was evaluated in 30 monitoring points and 7 parameters, which are: arsenic, cadmium, copper, chromium, mercury, lead and zinc, which were compared according to the standards recommended by the Environmental Quality Guidelines for Sediments in freshwater bodies of Canada (Canadian Environmental Quality Guidelines - CEQG, 2002. Sediment Quality Guidelines for Protection of Aquatic Life - Fresh water according to Canadian Council of Ministers of the Environment (CCME)). The results of the evaluation, by grey clustering method and Shannon entropy, showed that 13 monitoring points resulted in good sediment quality, 1 monitoring point had moderate quality and 16 monitoring points presented poor quality; therefore, it can be concluded that the effluents and discharges of the mining activities that take place in the aforementioned location have a negative impact on environmental quality. Finally, the results obtained can be of great help for OEFA, the regional government, the municipalities and any other body that has oversight functions, since they will allow them to be more objective and precise decisions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".