Enterococci as a key parameter for water quality index: Purires River, Costa Rica
Bibliographic record
Abstract
Abstract This study used the Canadian Water Quality Index (CWI) to characterize water sampled at three points within the Purires River micro basin, Costa Rica. The first sampling point is located in a high zone with domestic agricultural activities, the second point around the mid-point of the flow of the river, and the third point at the lowest zone with extensive agricultural activities mainly centered on the production of fresh vegetables. Eleven physicochemical parameters (As, Cd, Cr, biochemical oxygen demand (BOD), chemical oxygen demand (COD), NH4+, NO3−, Pb, pH, percent saturation of dissolved oxygen (PSO), and total suspended solids (TSS)) and two microbiological parameters (fecal coliforms and enterococci) were evaluated. We evaluated three different Canadian Water Quality Indexes (CWIs): CWI-1 included only physicochemical parameters, CWI-2 included CWI-1 parameters plus fecal coliforms, and CWI-3 included CWI-2 in addition to enterococci. Statistical analysis of individual parameters showed significant differences between sampling sites. CWI-1 was unable to discriminate between the three sampling points, and characterized the water quality as ‘fair’. CWI-2 was only able to discriminate when the water contained high levels of chemical and microbiological contaminants, while CWI-3 adequately discriminated water quality at each of the sampling points. The evaluation of enterococci together with more traditional water quality parameters enabled better categorization of surface 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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".