Comparing Mountain Snowpack Depth Model Results from Different Airborne Laser Scanning Flight Path Samples
Bibliographic record
Abstract
The objective of this study is to evaluate the performance of an Airborne Laser Scanning (ALS) snow sampling strategy using two distinct flight paths within a mountainous watershed. Drivers of snow depth variability (canopy, elevation, topographic position index, aspect) were used to generate a classified snow accumulation unit (SAU) raster for the Westcastle watershed, Alberta (103 km2). A “Least Cost Path” (LCP) analysis and an “expert” three-transect selection (T3) were used to create two flight path scenarios that each sampled <18% of the watershed area and maximized the number of represented SAUs. Watershed “wall-to-wall” snow depth was predicted from the T3, LCP, and combined T3 + LCP sampling data using ESRI’s Forest Based Regression. The variance was ∼ 83% for each of the three FBR scenarios. However, validation of the watershed-wide observed versus FBR predicted snow depth at watershed-scale produced R2 = 0.72 and RMSE = 0.38 m for the combined T3 + LCP flight line and R2 = 0.66 (RMSE = 0.43 m) for T3 alone. The LCP sampling did not perform as well (R2 = 0.34, RMSE = 0.61 m), indicating grid cell-level SAU attributes need to be supplemented by latitudinal and longitudinal sampling that captures beyond grid cell-level hydro-climatological trends across the watershed. By flying sampling corridors, that capture land surface attributes representative of the spatial variability of snow depth, watershed-scale snow volumes can be predicted.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".