Investigating spatial variability of ground temperatures across coastal-continental and latitudinal gradients in Labrador, northeastern Canada
Bibliographic record
Abstract
Periglacial landscapes support land-based activities important to Labrador Innu and Inuit, such as hunting and foraging, and support habitats for a diversity of northern species in the region. However, these environments are undergoing rapid ecosystem change due to recent atmospheric warming, which has altered regional and local ground thermal dynamics, including permafrost stability. Changing ground temperatures may further contribute to broad environmental change, but minimal observations of ground temperatures exist from highlands across the region. The lack of regional ground temperature data contributes to the misrepresentation of Labrador within permafrost distribution maps and increases risks for major infrastructure developments. Uncertainty also surrounds how ground temperatures in Labrador are responding to increased shrub encroachment and changes in winter precipitation. This thesis seeks to characterise spatial variability of near-surface ground temperatures in Labrador's highland ecosystems and identify the local factors most influencing this variability. We measured ground temperatures across five highland sampling areas with varying continentalities and latitudes. Data from 100 GST sites were collected during the 2022-2023 hydrological year, along with remote sensing data and in situ observations of local vegetation, soil, and topographic conditions. Mean annual ground surface temperatures (MAGST), n-factors, and surface offsets were collected and analysed by sampling area, landcover, and snow accumulation regime. Random Forest (RF) models also assessed the relative importance of various field, climate, and remote sensing variables for predicting MAGST. This thesis provides a baseline of ground temperature conditions in Labrador’s highlands, providing insights for updated permafrost maps, environmental impact assessments, and improving predictions of the impact of climate change on local ecosystems.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".