Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain
Bibliographic record
Abstract
Abstract. Snow is a major contributor to stream flow in alpine watersheds and quantifying snow depth and distribution is important for hydrological research. However, direct measurement of snow in rugged alpine terrain is often impossible due to avalanche and rock fall hazard. A laser rangefinder was used to determine the depth of snow in inaccessible areas. Laser rangefinders use ground based light detection and ranging technology but are more cost effective than airborne surveys or terrestrial laser scanning systems and are highly portable. Data were collected within the Opabin watershed in the Canadian Rockies. Surveys were conducted on one accessible slope for validation purposes and two inaccessible talus slopes. Laser distance data was used to generate surface models of slopes when snow covered and snow-free and snow depth distribution was quantified by differencing the two surfaces. The results were compared with manually probed snow depths on the accessible slope. The accuracy of the laser rangefinder method as compared to probed depths was 0.21 m or 12% of average snow depth. Results from the two inaccessible talus slopes showed regions near the top of the slopes with 6–9 m of snow accumulation. These deep snow accumulation zones result from re-distribution of snow by avalanches and are hydrologically significant as they persist until late summer.
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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 | 0.001 | 0.003 |
| 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) | 0.000 | 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 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".