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Record W2085927064 · doi:10.5539/enrr.v4n4p238

The Effects of Polluted River Water to the Riverside Groundwater, Case in Niger River in Koulikoro

2014· article· en· W2085927064 on OpenAlexvenueno aff
Traoré Drissa

Bibliographic record

VenueEnvironment and Natural Resources Research · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterEnvironmental scienceWater resource managementHydrology (agriculture)Surface waterWater qualityWater resourcesPollutionRecreationLaundryPopulationEnvironmental engineeringGeographyGeologyEcology

Abstract

fetched live from OpenAlex

Ground water demand is increasing in many African nations due to a number of factors. The growth of population, climate change, increase pollution of rivers, and insufficient number of purifying stations and waste water treatment (or almost nonexistent) have pushed to the water authorities for exploitation of underground water. These underground /groundwater have a relationship with surface water. Then what can be the effects of polluted River to its riverside groundwater? To explore the answer of this question and for the prevention sustainable and a better integrated management of water resources, we will do in-depth study on “the relationship between river water and riverside ground”. In Koulikoro region the results of this research show that Surface waters have poor bacteriological quality, the amount of total coliforms is very high, and accordingly Niger River’s waters are not allowed for consumption without treatment. However the river water can be safely used for laundry, bath, sports and recreation. Generally the Groundwater quality is good despite increased salinity has been observed sporadically. We found also that for the entire region of Koulikoro the average infiltration rate is less than 19.8% of the gross rainfall.

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 score0.114
Threshold uncertainty score0.885

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 studies0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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