Combining UAV LiDAR and Terrestrial Laser Scanning to investigate the impact of shrub expansion on local-scale Arctic snowpack distribution.
Bibliographic record
Abstract
Climate warming induces shrub expansion on Arctic herb tundra, with effects on snow trapping and hence snow depth. We have used UAV-borne LiDAR and Terrestrial Laser Scanning (TLS) to investigate the impact of shrub height on snow depth at two close sites near Umiujaq, eastern Canadian low Arctic, where dwarf birch and willow shrubs are expanding on lichen tundra. The first site features lichen and high shrubs (50-100 cm), a moderate relief, and a snowpack averaging 95 cm in spring. The second site consists of lichen and low shrubs (20-60 cm), more pronounced topography, and a deeper snowpack (101 cm). Digital Terrain and Surface Models were acquired in early fall to obtain topography and vegetation height. A Digital Surface Model obtained in spring produced snow depth maps at peak depth. TLS over a 400 m2 area produced time series of snow depth throughout the winter. TLS data show preferential snow accumulation in shrubs, but also preferential melting in shrubs during fall warm spells and in spring. UAV data at the first site show a strong correlation between vegetation height and snow depth, even after snow depth has exceeded vegetation height. This correlation is not observed at the second site, probably because snow depth there is much greater than vegetation height. These data show the need to reconsider some paradigms on snow-vegetation interactions, for example that vegetation does not affect snow accumulation beyond its height.
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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 | 0.000 | 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".