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Record W4282011519 · doi:10.53550/eec.2022.v28i03s.006

Assessment of Water Quality at Al-Hammar Marsh before and after Flow Improvement

2022· article· en· W4282011519 on OpenAlexaboutno aff
K.A. Jafaar, T.A. Mohammad, hussen ali

Bibliographic record

VenueEcology Environment and Conservation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsMarshWater qualityEnvironmental scienceCulvertTotal dissolved solidsEnvironmental engineeringIrrigationHydrology (agriculture)Water resource managementWetlandEngineeringEcology

Abstract

fetched live from OpenAlex

Marshlands have been considered among the most productive ecosystems on Earth. Marshlands existingin Mesopotamia are recently acknowledged by the United Nations as one of the international heritage sites.The current research highlights the deterioration level in the water quantity of the Al Hammar marshwhich is one of the famous marshes in the south of Iraq. Recently, pipe culverts were constructed to reducethe salinity of the Al Hammar marsh. Data on Total Dissolved Solids (TDS), Electric Conductivity (EC), andPower of Hydrogen (pH) from February 2019 to February 2020 at five stations (distributed within Al Hammarmarsh) were used to assess the water quality before and after the construction of pipe culverts. Two standardswere used in the assessment: the Iraqi Water Quality Standard (IWQS, No.417) and the World HealthOrganization (WHO) Standards for drinking, irrigation, and aquatic life. In addition, the quality of ecologicalhealth in the Al Hammar marsh was assessed by using the Canadian Water Quality Index. Results showthat the quality of water in the Al Hammar marsh is not recommended to be used for irrigation while theaquatic life is at a threat level.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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