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Record W2330738342 · doi:10.1190/nsapc2015-058

Hydrogeophysical Investigation of a Rock Glacier

2015· article· en· W2330738342 on OpenAlexaffabout
L. R. Bentley, Masahisa Hayashi, Alexandra Mozil

Bibliographic record

VenueNear-Surface Asia Pacific Conference, Waikoloa, Hawaii, 7-10 July 2015 · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGlacierGeologyRock glacierGeomorphology

Abstract

fetched live from OpenAlex

Summary Predictions are that the Rocky Mountains of Canada will receive less snow, more rain and have more potential evapotranspiration as the climate warms. The reduction in snow pack means that late summer flows from alpine systems will depend on the groundwater cycle of recharge and release from storage. In order to understand groundwater storage and release processes, we need to understand the contribution of different hydrologic landscape units. In this talk, we present results of a hydrogeophysics study of a rock glacier in the Helen Creek watershed located in Banff National Park, Alberta. As Helen Creek passes the rock glacier, the temperature declines 6°C, the electrical conductivity increases from 105 to 120 mS/cm and the flow increases by 35%. The changes occur in the region of a spring that discharges from the base of the rock glacier. The rock glacier occupies only 5% of the watershed contributing areas so we hypothesize that rock glaciers play an important role in the storage and late summer release of groundwater in alpine valleys. Electrical resistivity tomography, ground penetrating radar, seismic refraction tomography and surface temperature data have been collected over the rock glacier to help delineate the bedrock surface, establish groundwater flow paths and to identify any remnant ice in the rock glacier.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.041
GPT teacher head0.236
Teacher spread0.195 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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