Avatech: The First Portable, Web Connected Snow Penetrometer for Professionals
Bibliographic record
Abstract
Understanding snowpack stratigraphy and stability, its spatial and temporal variability, and the associated avalanche risks is inherently difficult, time consuming and one of the greatest challenges snow professionals face. For decades, snow professionals have recognized the need to develop field tools that can gather fast, quantitative, and accurate stratigraphic information about the snowpack which, in combination with other observations, provides invaluable information about slope stability. Current tools like the SnowMicroPen (SMP) and Ram Penetrometer have done wonders for the snow science community, but have not been broadly adopted by professionals due to high costs, difficulty of use, or other constraints. At AvaTech, we have developed the first portable, web-connected, and affordable snow penetrometer, the SP1, designed to quickly and accurately sample, record, and evaluate snow- pack structure and other critical snowpack characteristics. AvaTech measurement data is automatically synched via bluetooth to a smartphone application and then to the cloud, creating a unique crowd- sourced database of geographically based, snow conditions. Sharing this data across a broad network has the potential to create one of the largest sets of spatial and temporal snowpack information in the world, a potential high value resource for avalanche forecasting, snow hydrology, snow ecology, glaciolo- gy, and remote sensing applications. In our paper, we share qualitative and quantitative results from a rigorous scientific testing program with over 50 professional partner organizations across the US (CO, UT, CA, MT, WY, ID, AK, NH, VT), Canada, Norway, Iceland, Chile, Greenland and Switzerland. Specifically, we highlight high correlations between probe data and professional manual snowpit assessments as well as key learnings from testing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".