Model calibration for mapping permafrost using Landsat-5 TM and RADARSAT-2 images
Bibliographic record
Abstract
Permafrost is an important ground condition in high latitudes. Climate warming may lead to thickening of active layer, reducing permafrost thickness and extent, melting ground ice, causing ground subsidence and thermokarst erosions. In order to better map the distribution and dynamics of permafrost, there is a need to develop and test permafrost models that can be used with high spatial resolution remote sensing data. The purpose of this study is to calibrate the Northern Ecosystem Soil Temperature (NEST) model over the Victor Mine area located in the Hudson Bay Lowlands, Northern Ontario, Canada. The area is near the southern margin of permafrost region where permafrost exists only in isolated patches. We estimated and calibrated model input parameters using data from 1932 to 2012. The outputs were compared to field observations acquired between 2009 and 2012 at seven peat monitoring stations and two flux towers. Simulated soil temperatures show good agreement with observations at various depths for the different peatland types. The model shows the existence of permafrost only at palsa sites, which is in agreement with field observations. The calibrated model will be used to map permafrost over the whole area using remote sensing images.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".