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Record W4402832999 · doi:10.1080/15320383.2024.2408005

Ecological Risk Assessment to Aquatic Life from Metals in the Surface Sediments of the Santiago-Guadalajara River Basin, Mexico

2024· article· en· W4402832999 on OpenAlexaff
José de Anda, Luis Alberto Olvera-Vargas, Ofelia Yadira Lugo-Melchor, Harvey Shear

Bibliographic record

VenueSoil and Sediment Contamination An International Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStructural basinEnvironmental scienceHeavy metalsDrainage basinAquatic ecosystemEcologySedimentGeographyHydrology (agriculture)Water resource managementEnvironmental protectionGeologyEnvironmental chemistryGeomorphologyBiology

Abstract

fetched live from OpenAlex

The Santiago-Guadalajara River (SGR) is one of the most polluted river systems in Mexico because there is still a significant amount of untreated industrial, agricultural and municipal wastewater discharged. A sediment monitoring campaign was carried out at 25 sampling points located in the Santiago and Zula rivers, as well as in their main tributaries. In this work, six indices and criteria were selected to assess the level of metal contamination in the main and tributary streams. The following sequence of metals of concern were identified (mean values in mg kg-1 dry basis): Zn (71.92)> Cu (35.22)> Cr (23.82) > Ni (14.95)> Pb (10.82) > As (2.82) > Cd (2.40) > Sb (2.06). Previous studies in the region agree that Al, Fe, Mn, and Ba are part of the natural lithologic composition of the basin, and what is found in the sediments is likely the result of the natural rock weathering processes rather than an anthropogenic contamination process. According to the proposed assess methods Cu, Cr, Ni, Pb, and Zn were found in low to moderate levels of contamination, and Sb and Cd were found in considerable to very high contamination levels in several of the monitored stations.

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)0.002
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 score0.050
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSoil and Sediment Contamination An International JournalSame topicHeavy metals in environmentFrench-language works237,207