Snow Depth Measurements from Remotely Piloted Aerial Systems - Mt. Cain - 2018 - British Columbia - Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.017 |
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 it