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Record W2105341912 · doi:10.4236/jwarp.2012.48077

Attempts to Answer on the Origin of the High Nitrates Concentrations in Groundwaters of the Sourou Valley in Burkina Faso

2012· article· en· W2105341912 on OpenAlexaff
Françis Rosillon, Boubacar Savadogo, Aminata Kaboré, Hortense Bado-Sama, Dayéri Dianou

Bibliographic record

VenueJournal of Water Resource and Protection · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Environmental Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiggingGroundwaterContaminationLatrineAquiferEnvironmental scienceWater wellPit latrineHydrology (agriculture)Water qualityPiezometerEnvironmental engineeringEnvironmental chemistryGeologySanitationGeographyChemistry

Abstract

fetched live from OpenAlex

Within the framework of the contract of Sourou River, a survey of the groundwater quality was performed through 7 campaigns of water sampling and analysis from 2006 till 2012. The water samples resulted from 23 drillings and 9 wells located in the Sourou Valley. Among the analyzed physico-chemical parameters, the nitrates concentrations observed were worrisome. Out of 32 water sources, 14 (44%) supplied a nitrates content superior to the WHO threshold value for drinking water (50 mg NO3/L). Very high concentrations, superior to 500 mg NO3/L with a peak in 860 mg/L, were observed. Given the important variations observed from a sampling point to another, a generalized contamination of the total aquifer was not possible. An individual diagnosis allowed to identify the possible causes of this degradation. Several sources of contamination, in connection with the anthropological activities, were observed near the water facilities (drillings/wells): animal and human wild defecation, presence of nontight latrines, solid waste, wastewater discharges. It is also advisable to wonder about the impact of the dynamite use for digging wells, this one being able to leave nitrates in the water. With regard to the intensive use of water from the strongly contaminated wells and drillings by the rural populations of Sourou, implementing protection areas within which would be eliminated the sources of contamination in addition to health education among populations could improve the situation. Care should also be taken in the use of nitrates explosives for digging new wells or drillings.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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

Citations10
Published2012
Admission routes1
Has abstractyes

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