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Record W4381827144 · doi:10.21271/zjpas.35.2.11

Assessment of Some Physico-Chemical Parameters and Heavy Metals in The Greater Zab River Path from Bekhma to Al Guwayr District in Erbil Province-KRI.

2023· article· en· W4381827144 on OpenAlexaboutno aff

Bibliographic record

VenueZANCO Journal of Pure and Applied Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersSalahaddin University-Erbil
KeywordsAlkalinityTotal dissolved solidsEnvironmental chemistryWater qualityNitrateChemistryNitritePhosphorusHeavy metalsEnvironmental scienceEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Fifteen physico-chemical parameters with heavy metals were evaluated from the Greater Zab River path within Erbil province in twelve sites over a period of 10 months (from April 2021 to January 2022). In the field, Air and water temperature measured by thermometer, Electrical conductivity, total dissolved solids, potential of hydrogen measured by portable EC and pH meter, While, the laboratory measurement included total hardness, calcium ion, magnesium ion, total alkalinity, chloride measured by titration method, nitrate, nitrite, phosphorus and sulfate measured by spectrophotometric method. Water Quality calculated by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). Heavy metals were analyzed by (AAS Perkins Elmer USA 1100D). The results of the present study shows that electrical conductivity, total alkalinity and phosphorus mean values were higher than the limits of WHO standards, While all remaining mean parameters were found in agreement for drinking purposes for WHO standards. The studied sites were classified under the hard waters. CCME WQI status for all sites located under the category of “Fair” (65-79) except for site 12 was located under the category of “Marginal” (45-64) water quality. The decreasing trend of heavy metals were observed in the water as Si > Fe > Cr > Zn > Mn > Pb > Ni > Co > Cu > Cd.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.254

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 head0.030
GPT teacher head0.290
Teacher spread0.260 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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