Uncovering the Ground Thermal Regime of Coastal Labrador: The Influential Effects of Snow and Vegetation on Ground Temperatures
Bibliographic record
Abstract
The ground thermal regime of Arctic and Subarctic regions is impacted by climate and local scale factors. Snow cover has been linked to differences in ground temperatures over short distances (< 5 m) because of its thermal buffering properties which slows energy exchanges between the ground and atmosphere. Accurate derivation and characterization of the effects of snow cover on the ground thermal regime is essential for predicting the future impacts of climate change in northern Canada. This thesis presents a machine learning-based method for estimating snow cover from local ground surface temperature (GST) and air temperature measurements and was tested using modelled and in situ data. Results were compared against two other commonly used snow prediction methods, which select thresholds of either 1) the standard deviation of GST or 2) the difference between the standard deviations of air and surface temperatures. The machine learning method showed better performance for the modelled data and comparable performance with the in situ data compared to the other techniques. Variations in snow and ground temperatures were further analyzed with extensive field investigations at two field sites in coastal Labrador which includes a permafrost probability analysis. Results showed that mean annual ground surface temperatures (MAGST) was significantly correlated (p < 0.05) at our southern site with microclimate indices (freezing n-factor [r=-0.70], surface offset [r=0.99], nival offset [r=0.71]) while our northern site had significant correlations with both in situ ecosystem focused indices (snow depth [r=0.78], snow water equivalent [r=0.77]) and microclimate indices (freezing n-factor [r=-0.93], surface offset [r=0.99], nival offset [0.93]). Permafrost probability results showed a 10% likelihood across all 35 logger locations with all probable permafrost locations within our northern site at tundra and wetland ecotypes of low snow accumulation. Overall, this research will improve our ability to model snow-ground interactions and offers a step forward in our understanding of ground thermal heterogeneity in coastal environments. This work will support the next generation of permafrost and ground thermal modelling 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 | 0.000 |
| 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".