Utilizing spectral induced polarization to identify the ice core of a pingo: a case study in Haines Junction, Yukon, Canada
Bibliographic record
Abstract
This paper presents a field study conducted in Haines Junction, Yukon, utilizing Spectral Induced Polarization (SIP) to investigate the subsurface properties of a pingo site and specifically identify its ice core.The effectiveness of SIP analysis was demonstrated using a FUCHS frequency domain instrument, which measured electrical impedance magnitude and phase shift angle at multiple frequencies (1.46 Hz-40 kHz).The main focus of the analysis centered on the results obtained from electrical impedance magnitude and phase shift angle inversion at 40 kHz and 1.46 Hz.The inversion results revealed the presence of high resistivity layers within the subsurface, similar to results that would be expected with electrical resistivity tomography (ERT).Additionally, the SIP data revealed that some areas with high resistivity also had negative phase shift angle values, suggesting the presence of materials with polarizing properties.The analysis of the imaginary part of electrical impedance at 40 kHz for these areas highlighted the contribution of polarization, indicating the presence of ice.Furthermore, the electrical impedance magnitude at 1.46 Hz exhibited similarities to the 40 kHz analysis, but with higher resistivity.This pattern is another indicator of the presence of ice within the subsurface of the study area and was expressed using the Resistivity Frequency Effect (RFE) equation.The RFE analysis and the patterns of polarization confirmed the presence of the pingo ice core and identified its distinctive signature compared to other layers.Subsequent drilling confirmed the presence of interbedded ice and clay from 2.4-3.6 m depth and massive ice from 3.6-8.3m depth. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".