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Record W4321615109 · doi:10.1038/s43247-023-00697-6

Groundwater deeper than 500 m contributes less than 0.1% of global river discharge

2023· article· en· W4321615109 on OpenAlexafffund
Grant Ferguson, Jennifer C. McIntosh, Scott Jasechko, Ji‐Hyun Kim, J. S. Famiglietti, Jeffrey J. McDonnell

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsGlobal Institute for Water SecurityUniversity of CalgaryUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced ResearchGlobal Water FuturesNational Science Foundation
KeywordsStreamflowGroundwaterWater cycleHydrology (agriculture)Environmental scienceGlobal changeGeologyOceanographyClimate changeGeographyDrainage basinEcology

Abstract

fetched live from OpenAlex

Abstract Groundwater is one of the largest reservoirs of water on Earth but has relatively small fluxes compared to its volume. This behavior is exaggerated at depths below 500 m, where the majority of groundwater exists and where residence times of millions to even a billion years have been documented. However, the extent of interactions between deep groundwater (>500 m) and the rest of the terrestrial water cycle at a global scale are unclear because of challenges in detecting their contributions to streamflow. Here, we use a chloride mass balance approach to quantify the contribution of deep groundwater to global streamflow. Deep groundwater likely contributes <0.1% to global streamflow and is only weakly and sporadically connected to the rest of the water cycle on geological timescales. Despite this weak connection to streamflow, we found that deep groundwaters are important to the global chloride cycle, providing ~7% of the flux of chloride to the ocean.

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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.023
GPT teacher head0.219
Teacher spread0.196 · 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

Citations35
Published2023
Admission routes2
Has abstractyes

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