Modelled Soil Temperature Sensitivity to Variable Snow and Vegetation Conditions in Low-Relief Coastal Mountains, Nunatsiavut and NunatuKavut, Labrador
Bibliographic record
Abstract
Understanding permafrost vulnerability and resilience to climate warming is critical for predicting impacts on northern communities and ecosystems. The thermal characteristics of near-surface permafrost are influenced by effects from overlying vegetation and snow cover, both of which are changing in northern environments. The association between vegetation and snow is important in the coastal mountains of Labrador, northeast Canada, because of high annual snowfall totals and greening tundra biomes. In this study, we present a series of one-dimensional simulations using the Northern Ecosystem Soil Temperature (NEST) model to characterize ground thermal conditions at two field sites (Nain, Nunatsiavut & Pinware, NunatuKavut) along the Labrador coast. NEST simulations covering 1979-2019 were run using ERA5 atmospheric reanalysis for three ecotypes (tundra, shrub, treed) with three different snow accumulation regimes (snow drifting away from site, no snow drift, snow drifting to site). At Nain, perennially frozen ground was present for all three ecotypes when snow cover was kept thin (drifting away) but was largely absent for the ecotypes when snow accumulation was higher. At Pinware, frozen ground was mostly absent except where snow cover was shallow (wind drifting away). For low-snow simulations, frozen bodies (< 20 m) persisted in all ecotypes during cold periods but only remained intact following warmer years for treed ecotypes. These results highlight the importance of spatial and temporal variability in snow cover on ground thermal regimes in coastal Labrador.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".