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Record W3198992236 · doi:10.17762/de.vi.4590

Design and Analysis of Mathematical Model for the Concentration of Pollution and River Water Quality

2021· article· en· W3198992236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDesign Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAdvectionDiffusionPollutionWater qualityPollutantEnvironmental scienceMathematical modelConvection–diffusion equationQuality (philosophy)Term (time)Hydrology (agriculture)Computer scienceEnvironmental engineeringMathematicsChemistryThermodynamicsStatisticsGeologyGeotechnical engineeringMathematical analysisPhysics

Abstract

fetched live from OpenAlex

This paper mainly focuses on the recent advances in the mathematical models that provide the ability to predict the contaminant concentration levels of river water. The study represents an attempt for the researchers to study the problem of pollution, and we think that these mathematical analyses would provide better planning for water quality control. The model consists of a pair of coupled reaction Advection-diffusion equations for the pollutant and dissolved oxygen concentrations. Numerical solutions are obtained and some important inferences are drawn through simulation study. The Advection-Diffusion equation is characterized by the reaction term whenever it depends on concentration of the contaminants and in this case the original single Advection-diffusion equation will evolve to be a system of equations. It is no ticked that the higher are diffusion and reaeration coefficients, the faster is the river purity.

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.

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)0.001
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: none
Teacher disagreement score0.649
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.049
GPT teacher head0.258
Teacher spread0.209 · 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