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Record W4403614341 · doi:10.1515/zpch-2024-0740

Evaluation of water quality and heavy metal contamination in Cauvery River: Tamil Nadu region India

2024· article· en· W4403614341 on OpenAlexaff
Kavitha Velusamy, Ragavendran Venkatesan, Suresh Sagadevan, M. Umadevi, Annaraj Jamespandi, Smagul Karazhanov, Joshua M. Pearce, Jeyanthinath Mayandi

Bibliographic record

VenueZeitschrift für Physikalische Chemie · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsWestern University
Fundersnot available
KeywordsTamilContaminationWater qualityWater resource managementEnvironmental scienceHeavy metalsGeographyEnvironmental chemistryBiologyEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract Comprehensive water quality control is a fundamental requirement for environmental preservation and the sustainable development of communities around the globe. To showcase the importance of local quality controls in identifying the sources of pollution, a case study was conducted to analyze the quality of drinking water from different locations along the Cauvery River from Mettur to Trichy (200 km) in Tamil Nadu, India. The quality of water samples from different locations was indexed and compared with the World Health Organization and Indian Standards of water quality. The results indicate some high local values of TDS, hardness, and chloride content. These high values may be due to effluents from industries, dying factories, and sewage from the urban areas on the banks of the Cauvery River. This is most prevalent near Mohanur, where industrial waste and effluents were directly linked into the river. The results emphasize the importance of local quality control for accurately pinpointing the factors affecting the environment.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.333
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

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