Determination of warm, sensitive permafrost areas in near‐vertical rockwalls and evaluation of distributed models by electrical resistivity tomography
Bibliographic record
Abstract
Abstract Alpine rockwalls with warm permafrost (near 0°C) are the most active rockfall detachment zones in the Mont Blanc massif (MBM, French Alps) with more than 380 recent events. Near‐vertical rockwall permafrost is spatially controlled by variations in rock fractures, snow cover, and microtopography. A reliable method to validate the distribution of permafrost in critical and unstable areas does not yet exist. We present seven electrical resistivity tomography (ERT) surveys measured on five near‐vertical rockwalls in the MBM from 2012 and 2013 that have been calibrated with measurements on a granite sample in the laboratory. ERT shows consistent measurements of remaining sensitive permafrost relating to inferred temperatures from 0 to −1.5°C. ERT results demonstrate evidence of topographic controls on permafrost distribution and resistivity gradients that appear to reflect crest width. ERT results are compared to two permafrost index maps that use topoclimatic factors and combine effects of thin snow and fractures, where index model spatial resolution is crucial for the validation with ERT. In cryospheric environments, index maps seem to overestimate permafrost conditions in glacial environments. As a consequence, the sensitive areas of permafrost may slightly deviate from the results from distributed models that are only constrained by topoclimatic factors and interpreted with consideration of local fracture and snow conditions. This study demonstrates (i) that the sensitive and hazardous areas of permafrost in near‐vertical rock faces can be assessed and monitored by the means of temperature‐calibrated ERT and (ii) that ERT can be used for distributed model validation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".