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Record W4407694920 · doi:10.2166/wqrj.2025.058

Use of water quality indices in environmental management in Argentina

2025· article· en· W4407694920 on OpenAlexaboutno aff
Daniel Cicerone, Karina Paola Quaíni, Pablo Ezquerro

Bibliographic record

VenueWater Quality Research Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityQuality (philosophy)Environmental scienceBusinessWater resource managementEnvironmental planningEnvironmental resource managementEcologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.185
GPT teacher head0.432
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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