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Record W3194897317 · doi:10.1080/02626667.2021.1957479

Using vadose-zone water stable isotope profiles for assessing groundwater recharge under different climatic conditions

2021· article· en· W3194897317 on OpenAlexafffund
Lamine Boumaiza, Romain Chesnaux, Tarek Drias, Julien Walter, Christine Stumpp

Bibliographic record

VenueHydrological Sciences Journal · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGroundwater rechargeVadose zoneAridHydrology (agriculture)GroundwaterEnvironmental scienceGeologyDepression-focused rechargePrecipitationAquiferSoil waterSoil scienceGeography

Abstract

fetched live from OpenAlex

Considering three sites under different climate conditions (arid, semi-arid, and subhumid), this study aims to use the vadose-zone water stable isotope profiles to estimate the groundwater recharge rate. High-resolution vertical subsurface soil sampling along the vadose zone of the investigated sites was conducted. The collected samples were analysed to determine their stable isotope ratios (δ2H and δ18O) that were used along with the piston displacement method to estimate recharge. Annual recharge rates of 0.2% (± 0.1%), 2.5%, and 18% of the total annual precipitation were obtained for the arid, semi-arid, and subhumid sites, respectively. Recharge rates at the semi-arid and subhumid sites are comparable to those previously estimated using water balance-based methods. The recharge rate at the arid site is lower than that previously estimated for that site using the water budget-based method, revealing difficulties in applying the piston displacement method under an arid regime.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.087
GPT teacher head0.303
Teacher spread0.215 · 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

Citations16
Published2021
Admission routes2
Has abstractyes

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