The Hidden Image of Thawing Permafrost: project overview and first results of the radar polarimetric analysis
Bibliographic record
Abstract
Permafrost in the Northern hemisphere is rapidly warming in the context of climate change. The degradations associated to this trend pose several threats, locally to landscapes, infrastructures and settlements, and globally as permafrost is a potential source of greenhouse gazes in the carbon cycle. Different remote sensing methods can be used to monitor permafrost, most of them relying on surface observables which are then related to the ground thermal state. The Hidden Image of Thawing Permafrost (HIT Permafrost) project however aims at mapping directly subsurface properties using remote sensing data. We are aiming in particular at estimating soil properties such as ground ice content, layer composition and frozen versus non-frozen state of the soil in the sense of a vertical layering. To achieve this, the project relies on expert knowledge, ground measurements and remote sensing data combined using innovative techniques and models. The data has been collected over a particular test site, Trail Valley Creek, located in the Mackenzie River Delta (Canada). Airborne campaigns were performed simultaneously by the Alfred Wegener Institute (AWI) and the German Aerospace Center (DLR) in summer 2019 and winter 2019, providing a unique dataset of respectively optical photographs and LIDAR, and multimodal Synthetic Aperture Radar. We will give an overview of the HIT Permafrost project and present some first results of the radar polarimetric analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".