A new approach to mapping permafrost and change incorporating uncertainties in ground conditions and climate projections
Bibliographic record
Abstract
Abstract. Spatially detailed information on permafrost distribution and change with climate is important for land use planning, infrastructure development, and environmental assessments. However, the required soil and surficial geology maps in the North are coarse, and projected climate scenarios vary widely. Considering these uncertainties, we propose a new approach to mapping permafrost distribution and change by integrating remote sensing data, field measurements, and a process-based model. Land cover types from satellite imagery are used to capture the general land conditions and to improve the resolution of existing permafrost maps. For each land cover type, field observations are used to estimate the probabilities of different ground conditions. A process-based model is used to quantify the evolution of permafrost for each ground condition under three representative climate scenarios (low, medium, and high warming). From the model results, the probability of permafrost occurrence and the most likely permafrost conditions are determined. We apply this approach at 20 m resolution to a large area in Northwest Territories, Canada. Mapped permafrost conditions are in agreement with field observations and other studies. The data requirements, model robustness, and computation time are reasonable, and this approach may serve as a practical means to mapping permafrost and changes at high resolution in other regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".