Operational Techniques for Determining SWE by Sound Propagation through Snow
Bibliographic record
Abstract
Recent research has demonstrated that an acoustic pressure wave can be used to determine snow water equivalent (SWE) without the need for gravimetric sampling. The application of this technique poses a number of challenges in cold environments due to the presence of snow with wind crusts, ice layers, buried vegetation and high liquid water content and due to the extensive signal processing required after collection of returned sound waves. To show that the technique can contribute to operational SWE surveys, portable, field-usable devices were constructed with the capability of reliable on-line signal processing and calculation of SWE in the field. Reflections of the sound wave from the snowpack were identified by a peak detector algorithm and then analyzed. The acoustic method, portable field devices and modified signal processing techniques were tested at forest and tundra sites near Whitehorse, Yukon Territory, and at forest and meadow sites in the Rocky Mountains, Alberta, Canada. Comparisons were made between the acoustic technique and gravimetric sampling conducted using snowpits and density samples of individual snow layers and with bulk gravimetric sampling using an ESC30 “snow tube” snow density sampler and ruler. These comparisons demonstrated that the acoustic measurement with the portable field unit and on-line modified signal processing technique can provide SWE estimates in the field that are of comparable accuracy to SWE calculated from gravimetric samples. The on-line processing allows the operator to gauge the reliability of the measurement and to ensure sufficient data collection before leaving the field site. Significant advantages over gravimetric sampling accrue from non-destructive sampling of the snowpack and easy of measurement. Limitations and aspects for further research are also discussed.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".