MétaCan
Menu
Back to cohort
Record W4414850474 · doi:10.1080/02626667.2025.2569672

Tracing groundwater’s footprint in African semi-arid lands: isotopic tools for aquifer management

2025· article· en· W4414850474 on OpenAlexaff
Fatima Abdelfadel, Mohammed Hssaisoune, Claude Fontaine, Yassine Ait Brahim, Mohamed Qurtobi, Mohammed El Hafyani, Fatima Raibi, El Ghali Tibari, Meriém Bellarbi, Ayoub Ayaou, Lhoussaine Bouchaou

Bibliographic record

VenueHydrological Sciences Journal · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAquiferFootprintTracingHydrology (agriculture)Groundwater

Abstract

fetched live from OpenAlex

In African arid and semi-arid lands, groundwater is a strategic resource. However, in some watersheds like Moulouya Basin in Morocco, the groundwater circulation and recharge mechanisms are still not well known. Hence, this study investigates the recharge and hydrodynamic interactions within aquifers through an integrated geochemical and isotopic approach. Hydrochemical facies reveal carbonate control on water chemistry, while elevated SO₄2−, NO₃−, and Cl− concentrations reach 465, 42, and 874 mg/L, respectively, indicating both geogenic inputs and anthropogenic impacts. Radiocarbon dating showed groundwater ages ranging from modern to over 30 000 years, revealing a dual system of shallow, vulnerable aquifers and deep fossil water bodies. Stable isotopes confirmed recharge from high-altitude zones in the Middle and High Atlas. A conceptual model highlights complex and connected multilayered aquifers with limited renewal potential. This work emphasizes the urgent need for integrated groundwater management to protect modern and ancient water resources in arid environments.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.028
GPT teacher head0.252
Teacher spread0.223 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueHydrological Sciences JournalSame topicGroundwater and Isotope GeochemistryFrench-language works237,207