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Record W2759564565 · doi:10.1002/2017wr021496

Interpreting Repeated Temperature‐Depth Profiles for Groundwater Flow

2017· article· en· W2759564565 on OpenAlexaff
Victor Bense, Barret L. Kurylyk, Jonathan van Daal, Martine van der Ploeg, Sean K. Carey

Bibliographic record

VenueWater Resources Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsMcMaster UniversityDalhousie University
Fundersnot available
KeywordsBoreholeHydrogeologyAdvectionGroundwater flowGroundwaterGeologyTransient (computer programming)Soil scienceFlow (mathematics)Environmental scienceHydrology (agriculture)MechanicsGeotechnical engineeringAquiferThermodynamics

Abstract

fetched live from OpenAlex

Abstract Temperature can be used to trace groundwater flows due to thermal disturbances of subsurface advection. Prior hydrogeological studies that have used temperature‐depth profiles to estimate vertical groundwater fluxes have either ignored the influence of climate change by employing steady‐state analytical solutions or applied transient techniques to study temperature‐depth profiles recorded at only a single point in time. Transient analyses of a single profile are predicated on the accurate determination of an unknown profile at some time in the past to form the initial condition. In this study, we use both analytical solutions and a numerical model to demonstrate that boreholes with temperature‐depth profiles recorded at multiple times can be analyzed to either overcome the uncertainty associated with estimating unknown initial conditions or to form an additional check for the profile fitting. We further illustrate that the common approach of assuming a linear initial temperature‐depth profile can result in significant errors for groundwater flux estimates. Profiles obtained from a borehole in the Veluwe area, Netherlands in both 1978 and 2016 are analyzed for an illustrative example. Since many temperature‐depth profiles were collected in the late 1970s and 1980s, these previously profiled boreholes represent a significant and underexploited opportunity to obtain repeat measurements that can be used for similar analyses at other sites around the world.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.046
GPT teacher head0.330
Teacher spread0.283 · 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.

Study designNot applicable
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

Citations37
Published2017
Admission routes1
Has abstractyes

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