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Record W2521208405 · doi:10.2166/ws.2015.033

Assessment of the water quality of the Seybouse River (north-east Algeria) using the CCME WQI model

2015· article· en· W2521208405 on OpenAlexaboutno aff
Lamia Hachemi Rachedi, Hocine Amarchi

Bibliographic record

VenueWater Science & Technology Water Supply · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryWater qualityEnvironmental scienceSampling (signal processing)Drainage basinHydrology (agriculture)Surface waterIndex (typography)Structural basinWater resource managementGeographyEnvironmental engineeringGeologyGeomorphologyCartographyEcology

Abstract

fetched live from OpenAlex

This paper aims to assess the surface water quality of the Seybouse River using a model of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). The study area is located in the basin of the lower Seybouse River, north-east Algeria. The method involved the calculation of the WQI, based on the measurement of bacteriological and physico-chemical parameters. Water samples were collected from 13 sampling stations; observing the river and its most important tributary. The analysis of these samples showed that the water index of the river ranked as poor. The degradation of water quality of the river is mainly due to the lack of control over discharged materials and lack of water treatment.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.008
Scholarly communication0.0000.001
Open science0.0030.004
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 head0.056
GPT teacher head0.306
Teacher spread0.250 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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