Clasificación De La Calidad De Agua De La Región De Atacapi Utilizando Análisis Multivariado, En El Periodo 2019 – 2020.
Bibliographic record
Abstract
Water is the fundamental resource for life to develop, since it has two very important values. This research evaluated the quality of the hydric resource and the relationship that exists between the parameters evaluated in the Atacapi sector, in El Tena canton. For the execution of this study, three sampling points distributed throughout the Atacapi sector were determined, the first point in the upper part of the Pashimbi River, the middle part in the reservoir of the Amazonian State University (IKIAM). Data of physical-chemical and microbiological parameters were used as a result of monitoring in the wet season in three different months: February and May 2019, and January 2020, the information was subjected to exploratory analysis to define the correlation between the studied parameters. The quality of the water was determined through the scale based on the Canadian Council of Ministers of the Environment (CCME) Water Quality Index 1.0 methodology ; Establishing quality criteria based on current regulations and synthesizing the quality data matrix in a unique value, thus constituting a tool that allows simple analysis for politicians, technicians and the general public. The quality analysis determined that the water in the sector has a “good quality” rate, since the score is in the range 80 - 94. It was found that in the two sampling points located in the rivers that pass through the sector there is a “Total Coliforms and E – Coli quantity value” outside the maximum permissible limits. Finally, the relationship between the analyzed parameters was determined by clustering, forming in this way significant groups; one of the dependencies can be found between the "Sulfate and Electrical conductivity” parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".