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Record W2782821898 · doi:10.1002/2017gl076015

Tracking the Subsurface Signal of Decadal Climate Warming to Quantify Vertical Groundwater Flow Rates

2017· article· en· W2782821898 on OpenAlexaff
Victor Bense, Barret L. Kurylyk

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGroundwaterHydrogeologyGroundwater flowInflection pointBoreholeEnvironmental scienceGeologyGlobal warmingHydrology (agriculture)Inversion (geology)Climate changeSoil scienceAquiferGeomorphologyGeotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract Sustained ground surface warming on a decadal time scale leads to an inversion of thermal gradients in the upper tens of meters. The magnitude and direction of vertical groundwater flow should influence the propagation of this warming signal, but direct field observations of this phenomenon are rare. Comparison of temperature‐depth profiles in boreholes in the Veluwe area, Netherlands, collected in 1978–1982 and 2016 provided such direct measurement. We used these repeated profiles to track the downward propagation rate of the depth at which the thermal gradient is zero. Numerical modeling of the migration of this thermal gradient “inflection point” yielded estimates of downward groundwater flow rates (0–0.24 m a −1 ) that generally concurred with known hydrogeological conditions in the area. We conclude that analysis of inflection point depths in temperature‐depth profiles impacted by surface warming provides a largely untapped opportunity to inform sustainable groundwater management plans that rely on accurate estimates of long‐term vertical groundwater fluxes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.429
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

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

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 head0.062
GPT teacher head0.330
Teacher spread0.268 · 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 teacher head, 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

Citations42
Published2017
Admission routes1
Has abstractyes

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