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Record W2766925428 · doi:10.3997/2214-4609.201702023

Time-lapse Electrical Resistivity Surveys and Unfrozen Water Content in Cold Continuous Permafrost

2017· article· en· W2766925428 on OpenAlexaffabout
Greg A. Oldenborger, A -M LeBlanc

Bibliographic record

VenueProceedings · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsPermafrostElectrical resistivity and conductivityElectrical resistivity tomographyWater contentEnvironmental scienceArcticForcing (mathematics)Soil scienceAtmospheric sciencesGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Summary Estimates of material properties such as ice content or unfrozen water content are critical for thermal modelling of the response of permafrost to climate forcing, understanding contaminant flow and transport, or for predicting the behaviour of permafrost as an engineering substrate. We utilize time-lapse electrical resistivity surveys to examine the potential for imaging relative changes in unfrozen water content for cold continuous permafrost in the Canadian Arctic. Electrical resistivity data were collected from 2012/08 to 2015/06 at semi-regular time intervals using a permanent electrode installation at Iqaluit International Airport in Iqaluit, Nunavut. Using postinversion model differencing, we observe significant changes in electrical resistivity and we infer changes in unfrozen water content that appear consistent with temperature records. The most prevalent changes in resistivity are not limited to the active layer, but extend from 1–8 m depth in a zone of significant temperature fluctuation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.189
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

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

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 head0.045
GPT teacher head0.240
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes2
Has abstractyes

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