Snow Depth Measurements from Remotely Piloted Aerial Systems - Mt. Cain - 2018 - British Columbia - Canada
Bibliographic record
Abstract
This dataset consists of 10cm spatial resolution raster snow depth maps from Mt. Cain BC, collected in 2018. The dataset also includes snow depth validation points. Raster and point cloud data are available by request. We use Phantom 3 and 4 UAVs to map snow depth at 4 sites on Mt. Cain, BC. The imagery is processed with Pix4D and LASTools to create snow depth maps. The sites were mapped in winter, spring, and summer. When imagery is captured with optimal lighting conditions (few or no clouds, bright sun), it is possible in open areas to create accurate ground models. Of the 50 transects (25 x 2 visits). The average absolute deviation for each transect was: 28% less than 10cm, 70% less than 30cm. The method is challenged when there are extensive trees, fresh snow, and when there is flat light. In these cases there can be errors in the order of several meters. The overall root-mean-square error (RMSE) ranged from 30-70cm, however this could be substantially improved by removing areas with trees and excessive noise. Floyd, B., McInnes, W., Holmes, K., Cebulski, A., Dickinson, T., Butler, S., Heathfield, D. and Menounos, B. (2019). Application of UAVs to measure snowpack using structure from motion analysis over varying terrain and vegetation in Coastal British Columbia. [access date].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".