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Record W2472689704

Ccme Water Quality Index in River Cauvery Basin at Talakadu, Southindia

2016· article· en· W2472689704 on OpenAlexaboutno aff
S Umamaheshwari

Bibliographic record

VenueMyPrints@UOM (Mysore University Library) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsFecal coliformWater qualityHydropowerWater resource managementEnvironmental scienceHydrology (agriculture)GeographyEcologyBiologyGeology
DOInot available

Abstract

fetched live from OpenAlex

Cauvery a sacred river of South India which is considered as “Dakshina Ganga” for Hindu devotees is also an important source for agriculture and hydropower. The study was carried out to know the Water Quality Index (WQI) of river Cauvery, at Talakadu in Karnataka during Panchalingadarshana festival. WQI was assessed based on physico-chemical parameters using Canadian Council of Ministers of the Environment. WQI was found to be 48.49 which indicated “marginal” value, i.e., frequently impaired and conditions departed from desirable level.Biological Parameters such as Total bacterial count and MPN were found to be higher than normal value. H2S test was positive indicating fecal contamination. Presence of planktons such as Synedra ulna, Fragillaria bicepsand Oscillatoria species confirmed the polluted state of water. The study provided the status of river quality \nand this can help us to preserve precious water resources by planning and executing for protection, management and/or making lifestyle adaptations for the benefit of the environment. Key words: Cauvery, CCME, Water quality index

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2016
Admission routes1
Has abstractyes

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