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Record W2117693501 · doi:10.1080/09593330.2012.660644

Impact of human activities on the physico-chemical quality of surface water and groundwater in the north of Marrakech (Morocco)

2012· article· en· W2117693501 on OpenAlexfundno aff
Rachid Oufline, Rachid Hakkou, Lahoucine Hanich, Ali Boularbah

Bibliographic record

VenueEnvironmental Technology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersCanada Research ChairsInternational Development Research CentreUniversity of Florida
KeywordsGroundwaterSurface waterLeachateEnvironmental sciencePollutionWastewaterWater qualityHydrology (agriculture)Water pollutionEnvironmental engineeringDry seasonEnvironmental chemistryGeologyGeographyChemistryEcology

Abstract

fetched live from OpenAlex

The aim of this study was to assess the impact of three sources of pollution (landfill leachate, wastewater and mining activities) on the physico-chemical characteristics of surface water and groundwater in the northern region of Marrakech (Morocco). Numerous groundwater samples and surface water (Tensift River) samples were collected during the dry season and analysed. The groundwater samples had a high conductivity, which varied between 0.95 and 7.40 mS/cm; the conductivity of the surface water samples varied between 1.31 and 15.84 mS/cm. pH varied between 6.64 and 8.10 for groundwater and between 6.70 and 8.40 for surface water. The results showed that groundwater and surface water had a degraded quality in the region. Principal component analysis (PCA) enabled identification of the impact of pollution sources by combining the upstream and the downstream points. These results also showed that, in the study area, the effect of wastewater and the mine were dominated those of the landfill.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.232
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2012
Admission routes1
Has abstractyes

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