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Record W4405422317 · doi:10.33765/thate.15.1.3

Comparative assessment of groundwater quality in Kokrajhar, India

2024· article· en· W4405422317 on OpenAlexaboutno aff
Yachang Omo, Swdwmsri Brahma

Bibliographic record

VenueThe holistic approach to environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsWater resource managementGroundwaterQuality (philosophy)Quality assessmentEnvironmental planningEnvironmental scienceGeographyEngineeringEvaluation methodsReliability engineeringPhilosophyGeotechnical engineeringEpistemology

Abstract

fetched live from OpenAlex

A study was carried out to analyse and compare the quality of groundwater in Kokrajhar during the pre- and post-monsoon seasons. Kokrajhar is experiencing a significant population growth and urbanisation. Groundwater is vital for the daily activities and consumption of the local population. In order to meet the needs of the local population, it is crucial to evaluate the quality of groundwater. The study involved the collection of groundwater samples from 20 different locations during the pre- and post-monsoon periods. Tests were conducted on groundwater samples to analyse 10 different test parameters. Sample points were located using geographical information system (GIS) and contour maps were generated to represent different test parameters. The assessment of groundwater quality was determined using two different methodologies: the Weighted Arithmetic Index (WAI) method and the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method. The results obtained by both methods were compared. Minor to significant variations in various test parameters were observed during analysis of pre- and post-monsoon groundwater samples.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.112
GPT teacher head0.366
Teacher spread0.254 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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