Evaluación de las tendencias de la contaminación del recurso hídrico de la parte alta de la microcuenca del río Cutuchi, en la provincia de Cotopaxi, periodo 2019-2020.
Bibliographic record
Abstract
The pollution of rivers is a very common problem nowadays, being necessary tools that allow to determine the degree of pollution in a river. This research aims to evaluate the water quality in the high part of the Cutuchi River micro-watershed using the Canadian Council of Ministers of the Environment (CCME) and National Sanitization Foundation (NSF) methods. Thus, such as the application of the variance analysis through the completely randomized design and means comparison tests (Tukey). The analysis of water quality trends was also carried out using the Spearman`s Rho non-parametric test for a monthly period from 2010 to 2011. Seven evaluation points were established, distributed along the upper part of the micro-watershed. The results allowed identifying that the parameters BOD5 (24.47 mg/l), dissolved oxygen (19.9%) and fecal coliforms (1507.93 NMP) are out of the maximum permissible limits established in the TULSMA. According to the ICA-CCME and ICA-NSF methods, the point with the best water quality is located in the Cutuchi river, Quebrada San Rafael, San Ramon sector, for the period 2010 to 2011, determining a medium to good quality. The trend analysis identified the Cutuchi River, Ex-Navisco sector, as a point that presented an improvement in water quality ranging from bad to medium (p<0.01).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".