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Record W4206622911 · doi:10.31807/tjwsm.831510

A Flexible Water Quality Modelling Simulator Based on Matrix Algebra

2021· article· en· W4206622911 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTurkish Journal of Water Science and Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsComputer scienceWater qualityQuality (philosophy)SoftwareReadabilityWatershedComponent (thermodynamics)SimulationEcologyMachine learning

Abstract

fetched live from OpenAlex

For the sustainable management of aquatic ecosystems, an integrated approach is required. This is why watershed-based management is becoming an increasingly popular instrument for the improvement of water quality. Water quality models serve as a central part of the watershed management. Predictive water quality models are valuable tools, but they are usually complex infrastructures in terms of both operation and software development. The aim of this study is to develop the water quality simulator of a larger hydro-ecological modelling framework. Since the water quality problems are diverse, development of one water quality kinetics sub-model that would fit to all water quality problems would be an impossible task. This is the reason why; the water quality simulator software code was developed following the open source philosophy, implemented on a high level (yet high performance) programming language, and documented intensively in-line to enhance the code readability. The water quality simulator software, which is designed as a component of HIDROTURK integrated modelling platform, consists of a general transport sub-model, three water quality kinetics sub-models and utilities.

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)0.002
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 score0.376
Threshold uncertainty score0.483

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)0.0000.000

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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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