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Record W2072239155 · doi:10.1029/2006jf000585

Geophysical mapping of ground ice using a combination of capacitive coupled resistivity and ground‐penetrating radar, Northwest Territories, Canada

2008· article· en· W2072239155 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsMcGill University
FundersArcticNet
KeywordsGround-penetrating radarGeologyGeophysicsRadarRemote sensingElectrical resistivity and conductivityGeomorphologySeismologyPhysics

Abstract

fetched live from OpenAlex

The nature and distribution of ground ice are two of the most unpredictable geological variables in near‐surface deposits characterized by continuous permafrost. Subsurface information about ground ice distribution and structure can be obtained either by invasive and environmentally destructive techniques like drilling and excavation or by noninvasive low‐impact geophysical methods. In this study, coordinated measurements by two complementary geophysical tools, capacitive‐coupled resistivity (CCR) and ground‐penetrating radar (GPR) were used to map ground ice in a variety of locations in the Mackenzie Delta region of the western Canadian Arctic. Both CCR and GPR systems are highly portable (especially on snow covered surfaces) and very effective in collecting data under winter conditions when cold ground temperatures ensure that nearly all liquid water is frozen and signal penetration is enhanced. CCR and GPR readily detect stratigraphic differences including the contacts between massive ice deposits and enclosing sediments. GPR is widely used in permafrost research, but CCR has been used in only a few studies. This is the first study to combine results from both systems by collecting complementary data sets along coincident transects. We demonstrate that when combined, these data increase the quality and interpretation of subsurface information beyond what could be determined by either of the instruments alone. The complementary nature of these two geophysical tools facilitated the detection and mapping of massive ground ice, ice‐rich sediments, ice wedges, thermokarst, and basic stratigraphic relationships. This study breaks new ground by documenting the benefits of using these techniques together in permafrost investigations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.958
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.298
Teacher spread0.254 · 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