Evaluating local drivers of ground surface temperature variability in coastal Labrador
Bibliographic record
Abstract
Disentangling the contrasting influences of local ecosystem properties on soil temperatures is critical to understanding drivers of spatial variability in ground thermal conditions and permafrost distribution.In this study, we investigate the influence of vegetation, snow, and soil conditions on ground surface temperatures at two subarctic research basins in coastal Labrador.Eighteen ground surface temperature loggers were deployed at each basin using a stratified random deployment protocol based on land cover and snow thickness strata obtained from summer and winter uncrewed aerial vehicle surveys.Site-specific field information was also collected at each logger location in both summer and winter to contextualize ground surface temperature variability.At the southern field site (Pinware River Hills), derived microclimate indices had statistically significant associations with ground temperatures, whereas at the northern site (Nain Bay Hills), ecosystem properties including snow thickness, were important predictors of temperature variability.Ensemble model simulations of permafrost probability indicate that only 10% of loggers had more than 50% probability of having permafrost, but all of these loggers were found at Nain Bay Hills in low-snow tundra and wetland ecotypes.This research will inform regional permafrost distribution modelling and our understanding of the microclimate drivers or ground temperature variability in northern regions.1
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".