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Record W4283716236 · doi:10.51304/baer.2022.6.2.192

Evaluating the Water Quality of Al-Chibayish Marsh, Southern Iraq by Using the Canadian Index CCME-WQI

2022· article· en· W4283716236 on OpenAlexaboutno aff
Azhar Al-Asadi

Bibliographic record

VenueBiological and Applied Environmental Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsMarshWater qualityEnvironmental scienceTotal dissolved solidsTurbiditySodium adsorption ratioTotal suspended solidsBiochemical oxygen demandIrrigationHydrology (agriculture)NitrateEnvironmental chemistrySewageEnvironmental engineeringWastewaterWetlandChemical oxygen demandChemistryAgronomyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Al-Chibayish Marsh is one of the most important marshes located in southern Iraq. Many sewage plants discharging into the marsh and affect its water quality. The present study aims at evaluating the water suitability for drinking, irrigation and other domestic uses. Water samples were collected monthly from six stations in the marsh during August 2018 to July 2019. Twenty variables were monitored to apply the Canadian index, there are including water temperature, pH, turbidity, electrical conductivity, total suspended solids , total dissolved solids, dissolved Oxygen, biological oxygen demand, total hardness, Calcium, Magnesium, Nitrate, active Phosphate, Sulfate, Chloride, Sodium, Potassium, Sodium adsorption ratio, Boron and fecal coliform bacteria. The findings showed that the water of Al-Chibayish Marsh was marginal to poor for general uses, while it was poor for drinking or irrigation at all stations. The Iraqi Marshes Protection Law against pollution must be enforced to prevent the obvious deterioration of the marsh water to improve its quality.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.266
GPT teacher head0.413
Teacher spread0.146 · 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.

Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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