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Record W1979840387 · doi:10.2134/jeq2002.1256

An Assessment of Stream Water Quality of the Rio San Juan, Nuevo Leon, Mexico, 1995–1996

2002· article· en· W1979840387 on OpenAlexaff
José Santos Flores Laureano, José Návar

Bibliographic record

VenueJournal of Environmental Quality · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of Toronto
FundersConsejo Nacional de Ciencia y TecnologíaNational Water Commission
KeywordsEnvironmental scienceWater qualityHydrology (agriculture)Fecal coliformPollutionWater pollutionSampling (signal processing)ContaminationSulfateEnvironmental engineeringEnvironmental chemistryGeologyChemistryEcology

Abstract

fetched live from OpenAlex

Good water quality of the Rio San Juan is critical for economic development of northeastern Mexico. However, water quality of the river has rapidly degraded during the last few decades. Societal concerns include indications of contamination problems and increased water diversions for agriculture, residential, and industrial water supplies. Eight sampling sites were selected along the river where water samples were collected monthly for 10 mo (October 1995-July 1996). The concentration of heavy metals and chemical constituents and measurements of bacteriological and physical parameters were determined on water samples. In addition, river discharge was recorded. Constituent concentrations in 18.7% of all samples exceeded at least one water quality standard. In particular, concentrations of fecal and total coliform bacteria, sulfate, detergent, dissolved solids, Al, Ba, Cr, Fe, and Cd, exceeded several water quality standards. Pollution showed spatial and temporal variations and trends. These variations were statistically explained by spatial and temporal changes of constituent inputs and discharge. Samples collected from the site upstream of El Cuchillo reservoir had large constituent concentrations when discharge was small; this reservoir supplies domestic and industrial water to the city of Monterrey.

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)0.004
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 score0.315
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.047
GPT teacher head0.343
Teacher spread0.296 · 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

Citations16
Published2002
Admission routes1
Has abstractyes

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