A Combined Geophysical Approach to Imaging Permafrost Across Varying Ground Types in the Inuvialuit Settlement Region
Bibliographic record
Abstract
Changing climate conditions are causing significant impacts on Arctic communities, rapidly changing the landscape and drastically impacting ecosystems and the infrastructure these communities rely on to sustain their way of life. Ground penetrating radar and passive seismic are two complementary non-destructive methods when used to investigate the near subsurface permafrost within these locations. Permafrost thaw being one of the greatest threats to such landscapes which have a significant permafrost distribution. Investigation of the permafrost is essential to understanding the vulnerability of the ground and infrastructure within these areas, with these two geophysical methods providing new insights into permafrost vulnerability and active-layer processes. This study uses data collected in August 2024 at Reindeer Point near Tuktoyaktuk, Inuvialuit Settlement Region, Canada, whilst the active layer was in a thaw state. Depth to top of the permafrost layer was found to be$1.0-1.7 ~\mathrm{m}, 1.4-2.8 ~\mathrm{m}$and$0.3-1.3 ~\mathrm{m}$, in areas of different ground type – one location of untouched ground, one of partial made ground and another of completely made ground. Studying the differences in depth to the top of the permafrost for these different ground types is an initial step to more comprehensive investigation of the variability of permafrost in relation to different infrastructure locations and a look into the interaction between infrastructure and permafrost thaw and further to determine methodology to determine whether we are finding massive ice or permafrost.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".