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Record W3092337322 · doi:10.31223/osf.io/hdrje

Danger of groundwater contamination widely underestimated because of shortcuts for aquifer recharge

2020· preprint· en· W3092337322 on OpenAlexaff
Andreas Hartmann, Scott Jasechko, Tom Gleeson, Yoshihide Wada, Bartolomé Andreo, Juan Antonio Barberá, Heike Brielmann, Lhoussaine Bouchaou, Jean‐Baptiste Charlier, W.G. Darling

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Victoria
FundersDeutsche Forschungsgemeinschaft
KeywordsGroundwater rechargeAquiferGroundwater contaminationContaminationGroundwaterEnvironmental scienceWater resource managementHydrology (agriculture)GeologyGeotechnical engineeringEcology

Abstract

fetched live from OpenAlex

Groundwater pollution threatens human and ecosystem health in many areas around the globe. Shortcuts to the groundwater through concentrated recharge are known to transmit short-lived pollutants into carbonate aquifers endangering water quality of around a quarter of the world population. However, the large-scale impact of such concentrated recharge on water quality remains poorly understood. Here we apply a continental-scale model to quantify the danger of groundwater contamination by degradable pollutants through concentrated recharge in carbonate rock regions. We show that in regions where concentrated recharge takes place, the percentage of non-degraded pollutants in groundwater recharge increases from <1% to around 10-50%. In those regions, pollutants like Glyphosate can exceed their permissible concentrations by up to 19 times when reasonable application rates are assumed. Our results imply that in times of continuing industrial agricultural productivity, shortcuts to the groundwater may result in a widespread and substantial reduction of usable groundwater volumes.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.262
Teacher spread0.207 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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Same topicGroundwater and Isotope GeochemistryFrench-language works237,207