Modelling and mapping permafrost at high spatial resolution in Wapusk National Park, Hudson Bay Lowlands<sup>1</sup>This article is one of a series of papers published in this CJES Special Issue on the theme of<i>Fundamental and applied research on permafrost in Canada</i>.<sup>2</sup>Earth Science Sector Contribution 20110058.
Bibliographic record
Abstract
Most spatial modelling of permafrost distribution and dynamics has been conducted at half-degree latitude/longitude or coarser resolution. Such coarse results are difficult to use for land managers and ecologists. Here we mapped permafrost distribution at 30 m × 30 m resolution for a region in the northwest Hudson Bay Lowlands using a process-based model. Land-cover types and leaf area indices were derived from Landsat imagery; peat thickness was estimated from elevation based on field measurements; and climate data were interpolated from station observations. The modelled active-layer thickness and permafrost extent compared well with field observations, demonstrating that modelling and mapping permafrost at a high spatial resolution is practical for terrains such as these lowlands. The map portrayed large variations in active-layer thickness, with land-cover type and peat thickness being the most important controlling variables. The modelled active-layer thickness on average increased by 37% during the twentieth century due to increases in air temperature and precipitation, and permafrost disappeared in some southern areas. The spatial scale of the permafrost maps developed in this study is close to that of the ecosystem and landscape features; therefore, the results are useful for land management and ecosystem assessment.
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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 | 0.001 | 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".