Modelling the spatial distribution of permafrost in Labrador–Ungava using the temperature at the top of permafrost
Bibliographic record
Abstract
Permafrost zonation in Labrador–Ungava ranges from very isolated patches through to continuous permafrost. Here we present a new estimate of the distribution of permafrost at high resolution (250 m × 250 m) using spatial numerical modelling supported by station data from 29 air and ground climate monitoring stations. Permafrost presence was estimated using a modified version of the temperature at the top of permafrost (TTOP) model. Mean ground surface temperatures were modelled using gridded air temperatures and a novel n-factor parameterization scheme that compensates for regional differences in continentality, snowfall, and land cover and is transferable to other Subarctic environments. The thermal offset was modelled using land cover and surficial material datasets. Predicted TTOP values for the average climate range regionally from −9 °C (for high elevations in northern Quebec) to +5 °C (for southeastern Labrador – Quebec). Modelling for specific temporal windows (1948–1962, 1982–1996, 2000–2014) suggests that permafrost area increased from the middle of the 20th century to a potential peak extent (36% of the total land area) in the 1990s. Subsequent warming is predicted to have caused a decrease in permafrost extent of one-quarter (95 000 km2), even if air temperatures rise no further, providing air and ground temperatures equilibrate. Zonal boundaries derived by upscaling the high-resolution model are highly scale dependent, precluding direct comparison with the Permafrost Map of Canada that was generated without the use of geographic information system based analyses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".