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Record W2203147928 · doi:10.36953/ecj.2014.151203

Application of CCME WQI to evaluate feasibility of potable water availability: A case study of Tehri dam reservoir

2014· article· en· W2203147928 on OpenAlexaboutno aff
R. Bhutiani, D. R. Khanna, Prashant Tyagi, Bharti Tyagi

Bibliographic record

VenueEnvironment Conservation Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHydropower, Displacement, Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsTotal dissolved solidsAlkalinityWater qualityTurbidityNitrateBiochemical oxygen demandEnvironmental chemistryEnvironmental scienceChemical oxygen demandChemistryChlorideTotal suspended solidsEnvironmental engineeringWastewaterEcology

Abstract

fetched live from OpenAlex

In the present research work, the Canadian Council of Ministers of the Environment water quality index 1.0 (CCME WQI 1.0) was applied to assess water quality of Tehri dam reservoir by using the drinking water standard prescribed by the WHO (1999) and BIS (IS:10500, 1991). The physico-chemical parameters, ions concentration and heavy metals concentration used in the index calculation were total dissolved solids, pH, alkalinity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, total hardness, calcium, chloride, phosphate, sulphate, nitrate, total coliform (MPN/100 ml), turbidity, zinc, manganese, lead, nickel, iron and chromium. It was observed during the course of study that at all the four sites BOD, phosphate and total coliform showed greater deviation from the objective values. Total coliform was found to be more deviated from the normal values. Few important parameters were observed beyond the permissible limit for many times. The values of water quality index have shown that most of the sites are not fit for drinking purpose. Finally it was concluded that reservoir water should not be consumed for drinking purposes frequently without proper treatment.

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)0.006
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 score0.184
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 science0.0000.000
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 head0.052
GPT teacher head0.402
Teacher spread0.350 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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