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Record W4403049171 · doi:10.21608/ejabf.2024.382128

Application of the Canadian Index (CCME WQI) to Assess Water Quality for Aquatic Life: A Case Study of Water Quality for the Khoser and Tigris Rivers in the North of Nineveh Governorate

2024· article· en· W4403049171 on OpenAlexaboutno aff
Al-Assaf et al.

Bibliographic record

VenueEgyptian Journal of Aquatic Biology and Fisheries · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersUniversity of Mosul
KeywordsIndex (typography)Water qualityFisheryEnvironmental scienceQuality (philosophy)Water resource managementEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

The present study aimed to assess the water quality of the Khosr and Tigris rivers for aquatic life by applying two mathematical models: the model proposed by Rodriguez de Bassoon and the Canadian model (CCMEWQI). Physical, chemical, and biological properties were estimated to calculate the values for these models. The results indicate that the Khosr River has poor water quality for aquatic life, with Canadian model values ranging between 30.9 and 41.5. In contrast, the Tigris River water at the comparison site was classified under the Marginal water category, with quality further deteriorating after the Khosr River merges with it, showing values between 52.7 and 40.2. Similarly, using Rodriguez de Bassoon’s model, the Khosr River was classified as having Bad water quality with values ranging between 26.35 and 35.96, while the Tigris River exhibited Medium water quality with values between 54.81 and 64.33. The deterioration in water quality was mainly attributed to a significant reduction in oxygen levels, reaching as low as 1.2mg/ L, coupled with an increase in organic load to 56mg/ L. This reflects a considerable degradation of the Khosr River, which negatively impacts the water quality of the Tigris River within the Mosul City area.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.062
GPT teacher head0.326
Teacher spread0.263 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEgyptian Journal of Aquatic Biology and FisheriesSame topicWater Quality and Pollution AssessmentFrench-language works237,207