Automated Determination of Snow Water Equivalent by Acoustic Reflectometry
Bibliographic record
Abstract
Snow water equivalent (SWE) is commonly determined using gravimetric and depth measurement techniques. Previous research has demonstrated the ability to determine SWE from the propagation and reflection of acoustic waves. Despite the advantages of the acoustic technique, it has not been adapted so that SWE can be determined in an automated fashion. This paper presents a new technique for determining SWE by the application of acoustics. A maximum-length sequence was used as the input to the layered snowpack system. Signal processing of the reflected wave and a recursive algorithm was used to model the sound pressure wave as it passed through the snowpack. Embedded systems were designed to implement the signal processing and calculations so that SWE could be quickly determined at a field location. The systems were deployed at sites near Whitehorse, YT, and at sites in the Rocky Mountains of Alberta, Canada. Comparisons were made between SWE estimated by the acoustic technique and SWE determined by gravimetric sampling. These comparisons demonstrated that the acoustic SWE measurement performed with the embedded systems and the new signal processing technique can provide SWE estimates that are of comparable accuracy to SWE calculated from gravimetric samples.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".