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Intensive rainfall recharges tropical groundwaters

2015· article· en· W2194803334 on OpenAlexafffund
Scott Jasechko, Richard G. Taylor

Bibliographic record

VenueEnvironmental Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNatural Environment Research CouncilSight Research UK
KeywordsGroundwater rechargeGroundwaterTropicsEnvironmental sciencePrecipitationHydrology (agriculture)PopulationTropical climateAquiferWater resource managementGeographyGeologyEcologyBiologyMeteorology

Abstract

fetched live from OpenAlex

Dependence upon groundwater to meet rising agricultural and domestic water needs is expected to increase substantially across the tropics where, by 2050, over half of the world's population is projected to live. Rare, long-term groundwater-level records in the tropics indicate that groundwater recharge occurs disproportionately from heavy rainfalls exceeding a threshold. The ubiquity of this bias in tropical groundwater recharge to intensive precipitation is, however, unknown. By relating available long-term records of stable-isotope ratios of O and H in tropical precipitation (15 sites) to those of local groundwater, we reveal that groundwater recharge in the tropics is near-uniformly (14/15 sites) biased to intensive monthly rainfall, commonly exceeding the ∼70th intensity decile. Our results suggest that the intensification of precipitation brought about by global warming favours groundwater replenishment in the tropics. Nevertheless, the processes that transmit intensive rainfall to groundwater systems and enhance the resilience of tropical groundwater storage in a warming world, remain unclear.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.065
GPT teacher head0.273
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

Citations186
Published2015
Admission routes2
Has abstractyes

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