Geophysical imaging and thermal modeling of subsurface morphology and thaw evolution of discontinuous permafrost
Bibliographic record
Abstract
Abstract Despite our current understanding of permafrost thaw in subarctic regions in response to rising air temperatures, little is known about the subsurface geometry and distribution of discontinuous permafrost bodies in peat‐covered, wetland‐dominated terrains and their responses to rising temperature. Using electrical resistivity tomography, ground‐penetrating radar profiling, and thermal‐conduction modeling, we show how the land cover distributions influence thawing of discontinuous permafrost at a study site in the Northwest Territories, Canada. Permafrost bodies in this region occur under forested peat plateaus and have thicknesses of 5–13 m. Our geophysical data reveal different stages of thaw resulting from disturbances within the active layer: from widening and deepening of differential thaw features under small frost‐table depressions to complete thaw of permafrost under an isolated bog. By using two‐dimensional geometric constraints derived from our geophysics profiles and meteorological data, we model seasonal and interannual changes to permafrost distribution in response to contemporary climatic conditions and changes in land cover. Modeling results show that in this environment (1) differences in land cover have a strong influence on subsurface thermal gradients such that lateral thaw dominates over vertical thaw and (2) in accordance with field observations, thaw‐induced subsidence and flooding at the lateral margins of peat plateaus represents a positive feedback that leads to enhanced warming along the margins of peat plateaus and subsequent lateral heat conduction. Based on our analysis, we suggest that subsurface energy transfer processes (and feedbacks) at scales of 1–100 m have a strong influence on overall permafrost degradation rates at much larger scales.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".