Utilizing the TTOP model to understand spatial permafrost temperature variability in a High Arctic landscape, Cape Bounty, Nunavut, Canada
Bibliographic record
Abstract
Abstract Ground surface and permafrost temperatures in the High Arctic are often considered homogeneous especially when viewed at the scale of climate and environmental models. However, this is generally incorrect due to highly variable, topographically redistributed snow cover, which generates a substantial degree of ground thermal heterogeneity. The objective of this study is to describe and spatially model the variability in the ground thermal regime within the Cape Bounty Arctic Watershed Observatory (CBAWO), Nunavut, Canada, using the TTOP model, for current conditions in addition to a series of future climate change scenarios. While observed air temperature was mostly uniform, annual mean ground surface and permafrost temperatures across the paired watersheds were estimated to range between −3.8 to −13.8°C and −3.9 to −14°C, respectively, similar to the −5 to −15°C magnitude and range identified from boreholes across the High Arctic. The spatial models showed higher ground surface temperatures in topographic hollows (slope bases and stream channels), and lower temperatures in areas of topographic prominence (hilltops and plateaus) following the spatial pattern of snow accumulation and redistribution. Under projected climate change, the models predicted areas with the coldest permafrost to have the largest magnitude of warming (about 9°C), while areas of warm permafrost became closer to 0°C (warming 4–7°C). This thermal heterogeneity may have implications for ground instability such as permafrost‐related mass movements, hydrological connectivity, biogeochemical cycling, and microbial activity, which influence water quality and contaminant mobility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".