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Record W3024608739 · doi:10.3390/w12051406

Modelling the Temporal Dynamics of Groundwater Pollution Risks at the African Scale

2020· article· en· W3024608739 on OpenAlexfundno aff
Issoufou Ouédraogo, Arthur Girard, Marnik Vanclooster, François Jonard

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersIslamic Development BankMcGill University
KeywordsPollutionGroundwaterEnvironmental scienceHydrogeologyWater resource managementScale (ratio)Hydrology (agriculture)Groundwater pollutionAquiferGeographyGeologyCartographyEcology

Abstract

fetched live from OpenAlex

Groundwater pollution risk modelling is an important asset to improve groundwater management and protection. In this study, we assess the temporal dynamics of groundwater pollution risk at the continental scale, using the DRASTIC model. The approach was developed using continental-scale data on soil properties, topography, land use, geology, hydrogeology, and climate with a resolution of 15 × 15 km2. We compared continental-scale groundwater pollution risk for the years 1990, 2000, and 2010. The results showed significant inter-annual variations of the spatial distribution of pollution risk. Changes were mainly concentrated in the area of the Nile Delta, around the Lake Victoria, in North Africa, and in coastal West Africa (predominately in Nigeria). We found that the increase in pollution risk was mainly related to the increase in the population density in these regions. The proposed methodology for modelling the temporal dynamics of groundwater pollution risk could support the monitoring of the Sustainable Development Goal 6, which focus in particular on the preservation of the freshwater resources against future threats.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.030
GPT teacher head0.201
Teacher spread0.171 · 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 designSimulation or modeling
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

Citations17
Published2020
Admission routes1
Has abstractyes

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