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Record W4372316806 · doi:10.52363/2522-1892.2023.1.9

ANALYSIS OF KNOWN METHODS OF DETERMINING OF THE WATER QUALITY INDEX SUITABLE FOR PREDICTING THE ENVIRONMENTAL STATE OF SURFACE WATER BODIES

2023· article· en· W4372316806 on OpenAlexaboutno aff
Svitlana Kovalenko, Роман Пономаренко, Oleg Tretyakov, Yevhen Ivanov

Bibliographic record

VenueTechnogenic and Ecological Safety · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Resources and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityZoningEnvironmental scienceIndex (typography)PollutionWater pollutionWater resource managementWater resourcesQuality (philosophy)Environmental qualityHydrology (agriculture)Environmental engineeringEnvironmental resource managementEnvironmental protectionEcologyCivil engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

The article considered the ecological index of water quality, which is used for planning water protection activities, developing water protection measures, carrying out ecological and ecological and economic zoning, ecological mapping; water pollution index, which is determined by hydrochemical indicators; modified Horton water quality index models; the Water Quality Index is proposed by the Canadian Council of Ministers of the Environment; the Said index, which is used to assess the quality of water for general use; water quality index in the river subbasin in a certain year; The Nemerov Pollution Index, which is used to comprehensively assess water, precipitation, or soil quality, and the Oregon Water Quality Index. Advantages and disadvantages of water quality and pollution indices are determined.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes1
Has abstractyes

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