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Record W3214155819 · doi:10.1063/5.0066552

Seasonal and annual variation of Tigris River’s water quality using physicochemical parameters within Baghdad city

2021· article· en· W3214155819 on OpenAlexaboutno aff
Omar Anmar Almoula, Khalid Adel Abdulrazzaq, Athraa Hashim Mohammed, Rami Raad Ahmed Al-Ani

Bibliographic record

VenueAIP conference proceedings · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTurbidityEnvironmental scienceWater qualityHydrology (agriculture)AlkalinitySeasonalityIndex (typography)Water resourcesStatisticsMathematicsGeology

Abstract

fetched live from OpenAlex

Water quality indices play an indispensable role in the holistic approach of managing water resources; it gives a straightforward interpretation of the vast range of water quality tests’ results. In the present study, the Nemerow pollution index and the Canadian water quality index were applied to analyze the spatial and temporal fluctuation of Tigris River’s water quality within Baghdad city (Nine observing stations were considered for a Six-year study period). Overall, a grade of slightly polluted/Marginal was attained; the seasonal analysis shown a decline in the water quality in winter and spring seasons. Beast water quality index values obtained at the Al-Karkh station in 2015 and were 3.51 (Grade C using Nemerow method) and 73.63 (Grade C using the Canadian method). Both Nemerow and the Canadian indices exhibited comparable results, producing a global trend of Tigris water quality deteriorates as it passes across the city. Among fifteen physicochemical parameters tested in this paper, the main water quality deterioration factors of the Tigris River were recognized to be: turbidity, iron, ammonia, sulfate, total dissolved solids, electrical conductivity, and alkalinity; where they exceeded permissible limits for drinking purpose, especially after Al-Karkh station during the study period.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.051
GPT teacher head0.292
Teacher spread0.241 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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