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Record W2342847482 · doi:10.12944/cwe.11.1.14

Seasonal Variation in Water Quality of Lukha River, Meghalaya, India

2016· article· en· W2342847482 on OpenAlexaboutno aff
R. Eugene Lamare, Om Pal Singh

Bibliographic record

VenueCurrent World Environment · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityDrainage basinEnvironmental scienceHydrology (agriculture)Deforestation (computer science)Catchment areaWater resource managementGeographyGeologyEcology

Abstract

fetched live from OpenAlex

Lukha River (Wah Lukha) is one of the major rivers of Meghalaya situated in the southern part of East Jaintia Hills District. Activities such as mining of coal and limestone, manufacturing of cement, deforestation etc. have been taking place in the catchment area of the river leading to changes in water quality. This is evident from the deep blue appearance of water of Lukha River during winter months for the last 7-8 years.Till date no convincing and conclusive reason has been given for this annual change in physical appearance.To get insight, we studied the physico-chemical water quality parameters of this river in different seasons and found that the water quality has started deteriorating due to activities occurring in the catchment area. Based on Canadian Council of Ministers of the Environment-Water Quality Index (CCME-WQI) the water of the river at some locations was found of ‘poor’ quality.

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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.283
Teacher spread0.250 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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