Investigating spatial variability of ground temperatures across coastal and continental highlands in Labrador, northeastern Canada
Bibliographic record
Abstract
Interactions between atmospheric warming, local surface conditions, and ground temperatures complicate efforts to predict future permafrost changes in regions such as Labrador, northeastern Canada, where ground temperature monitoring is limited. This study provides the first comprehensive investigation of factors influencing local-to-regional ground temperature variability in Labrador’s highland environments including in areas subject to infrastructure development. Ground surface temperature measurements ( n = 100), in situ field characteristics (i.e., vegetation, soil, and topography), and remotely derived variables were collected at five sampling areas of varying latitudes and continentalities. Our analysis identified no consistent latitudinal trend in mean annual ground surface temperatures (MAGST) or permafrost occurrence probabilities. Instead, the highest permafrost probabilities were found in dry, wind-blown areas with sparse vegetation cover in Labrador’s southwest interior. Using machine learning, we identified the most important climate variables (i.e., mean annual air temperature and air temperature range) and local site characteristics (i.e., maximum understory vegetation height, snow accumulation indices) for predicting regional variability in MAGSTs. These findings will support future geohazard assessments in similar highlands and aid in the development of fine-resolution permafrost distribution maps. These data also help to establish a critical baseline of thermal conditions for evaluating the impacts of further environmental and climatic changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".