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Record W4241738199 · doi:10.32920/ryerson.14643678

Evaluation of the performance of multiple water quality loading index systems for Sapgyo River

2021· preprint· en· W4241738199 on OpenAlexaffabout
Faizul Hasan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWater qualityIndex (typography)Quality (philosophy)Unit (ring theory)Environmental sciencePollutantSanitationComputer scienceWater resource managementEnvironmental economicsEnvironmental resource managementEnvironmental engineeringMathematics

Abstract

fetched live from OpenAlex

To enhance the sustainability of [the] water-quality-management system, the modeling results of simulated pollutants are needed to translate into an understandable single unit water quality index to help decision-makers. QUAL2E model is helpful in translating the results of simulated pollutants into a single water quality rating unit termed as "QUAL2E water quality loading index (QUAL2I)". This approach is adopted to evaluate the performance of National Sanitation Foundation’s Water Quality Index (NSFWQI) and Canadian Council of Ministers of the Environment’s Water Quality Index (CCMEWQI) using data set of Sapgyo River. CCMEWQI results are found better, especially for meeting the desired quality objectives. Additionally, a decision-making process has been suggested based on better found QWQLI result to maintain the whole river channel at acceptable water quality standards. The study results imply that further study should be carried out using [a] minimum [of] four variables, each having at least four test samples to compute QWQLI using CCMEWQI approach.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0000.001
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 head0.104
GPT teacher head0.339
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2021
Admission routes2
Has abstractyes

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