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Record W4298619375 · doi:10.31223/x5q64h

Global groundwater warming

2022· preprint· en· W4298619375 on OpenAlexafffund
Susanne A. Benz, Dylan J. Irvine, Gabriel C. Rau, Peter Bayer, Kathrin Menberg, Philipp Blum, Rob Jamieson, Christian Griebler, Barret L. Kurylyk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsDalhousie University
FundersCanada Research ChairsGovernment of Canada
KeywordsGroundwaterWater tableEnvironmental scienceAquiferBiogeochemical cycleHydrology (agriculture)Global warmingClimate changeSurface waterGeologyOceanographyEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Aquifers contain the largest store of unfrozen freshwater, making groundwater critical for life on Earth. Groundwater temperatures infl uence stream thermal regimes, groundwater-dependent ecosystems, aquatic biogeochemical processes, water quality, and the geothermal potential. Yet little is known about how groundwater responds to surface warming across spatial and temporal scales. We simulate current and projected groundwater temperatures at the global scale and show that groundwater at the depth of the water table is projected to warm on average by 3.3 ° C between 2000 and 2099 (RCP 8.5). However, regional groundwater warming patterns vary substantially due to spatial variability in climate and water table depth. The highest warming rates are projected in Central Russia, Northern China, and parts of North America and the Amazon rainforest. Results also show that by 2099, 234 million people are projected to live in areas where groundwater exceeds the highest threshold for drinking water temperatures set by any country.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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