Evaluating peatland permafrost characteristics and vulnerability along the Labrador Sea coastline using uncrewed aerial vehicles
Bibliographic record
Abstract
Permafrost in peatland environments often creates unique elevated landforms known as palsas and peat plateaus. These landforms provide important wildlife habitat, store large quantities of carbon, and are often used by Indigenous people (Innu and Inuit) living in coastal Labrador. Palsas and peat plateaus in southern Labrador are some of the most southern lowland permafrost features in the northern hemisphere, making them particularly vulnerable to thaw as global air temperatures continue to rise. However, assessing the thaw risk of peatland permafrost in the region remains difficult due to a lack of baseline geomorphological and ecological information. This thesis aims to address this knowledge gap by providing the first large scale examination of peatland permafrost characteristics along the Labrador Sea coastline. We conducted detailed site level analyses at 20 peatland permafrost complexes, spanning a latitudinal range from Blanc-Sablon (51.4°N) to Nain (56.5°N). For each site, we delineated the permafrost landform extents and extracted information about the features using high resolution imagery and structure-from-motion data products. We examined how characteristics such as landform height, extent, ice content, fragmentation, and vegetation height vary across the study region, and compared this to peatland permafrost landforms in other parts of the world. We also developed a relative permafrost resiliency index, which we used to evaluate spatial patterns in permafrost degradation states. This thesis offers novel methods for extracting geomorphological information using UAV data and provides comprehensive characterization of peatland permafrost in a previously understudied region.
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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".