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Record W3094492627 · doi:10.1029/2020gl090134

Using Thermal Springs to Quantify Deep Groundwater Flow and Its Thermal Footprint in the Alps and a Comparison With North American Orogens

2020· article· en· W3094492627 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicKarst Systems and Hydrogeology
Canadian institutionsUniversity of Saskatchewan
FundersDeutsche Forschungsgemeinschaft
KeywordsGroundwaterGeologyGroundwater flowHydrology (agriculture)Spring (device)ThermalFlow (mathematics)Environmental scienceAquiferGeotechnical engineeringMeteorologyMechanicsGeography

Abstract

fetched live from OpenAlex

Abstract The extent of deep groundwater flow in mountain belts and its thermal effects are uncertain. Here, we use a new database of discharge, temperature, and composition of thermal springs in the Alps to estimate the extent of deep groundwater flow and its contribution to the groundwater and heat budget. The results indicate that thermal springs are fed exclusively by meteoric water and make up 0.1% of the total groundwater budget. Spring water circulates on average to a depth of at least 2 km. The net heat extracted from the subsurface equals 1% of the background heat flow, which equals an average thermal footprint of 7 km 2 . Cooling by downward flow and heating by upward flow are three and two times higher than the net heat flow, respectively. Comparison with North American orogens shows that hydrothermal activity is higher in areas with high relief or areas under extension.

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.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.303
Teacher spread0.224 · 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