Bibliographic record
Abstract
Abstract A novel electronic sensing system (SAS2) was designed to non‐invasively and simultaneously measure snow density, liquid water content, and temperature of snow using acoustics and obtain images of the snowpack. The system is an updated and more sophisticated version of previous acoustic sensing systems, and this paper presents an update on the development of acoustic sensing methods to measure multiple snowpack properties and obtain images of snowpack stratigraphy. Air‐coupled acoustic waves were sent into and reflected from the snowpack by the SAS2 system. An inverse model based on a modified version of the Biot–Stoll theory of sound propagation through porous media was used to obtain acoustic measurements of snow and images of the snowpack. The SAS2 was deployed at ten field sites in the Canadian Rockies. Stationary and portable versions of the system were deployed and tested. This paper reports initial tests of the SAS2 that were conducted at the field sites to compare acoustic model outputs with gravimetric measurements of snow density, dielectric measurements of liquid water content, and thermocouple and thermometer measurements of snow temperature. Snow water equivalent could be estimated more accurately and quickly with the SAS2 than with an ESC30 gravimetric snow tube. Unlike monopulse sonar and radar sensors, the SAS2 utilizes a continuous‐wave Maximum Length Sequence (MLS) that is more robust to environmental noise. The SAS2 serves as a proof‐of‐concept of the acoustic snow measurement technique with potential for further research as identified and discussed in this paper. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".