Use of water quality indices in environmental management in Argentina
Bibliographic record
Abstract
ABSTRACT The aim of this work is to provide the contribution of water quality indices (WQIs) to environmental management of water resources, during the last three decades in Argentina. As part of the Latin America and the Caribbean region, one of the most water-rich regions in the world, monitoring and management of water stress has not always received enough attention. Particularly, if it is taking into account that due to high temporal and geographic variability in water distribution, it was, it is and it will be the main driver for the development of the activities of the country. A summary of the role of key actors involved in the integral management of water resources is presented, with particular emphasis in those ones responsible of the implementation of water quality monitoring programs and the management of environmental data coming from them. Finally, this work presents different WQIs that have been used in Argentina to assess decision-making. Two case studies (Matanza-Riachuelo River basin and Río de la Plata River) have been selected to show how one of them, the WQI of the Canadian Council of Ministers of Environment, has been implemented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".