Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/hyp.6793 Determining snow water equivalent by acoustic sounding
Bibliographic record
Abstract
The possibility of determining snow water equivalent (SWE) by the use of an acoustic impulse was assessed at two field locations in Saskatchewan and British Columbia, Canada. These sites represent cold windswept prairie and temperate deep mountain snowcovers. A continuous frequency-swept acoustic wave was sent into the snowpack and received. Signal processing was then subsequently used to estimate the depth and density of each snow layer by a recursive relationship involving frequency-modulated continuous-wave (FMCW) radar and seismological techniques. From this method, it is also shown that the tortuosity of snow can be estimated. Data collected by gravimetric sampling was used as comparison to the SWE values determined by the use of acoustic sounding. The results showed that for the Saskatchewan sites, the correlation between the measured and the modeled values of SWE was 0Ð86, whereas at the British Columbia sites, the correlation was 0Ð78. The difference in the correlations was interpreted as being due to additional acoustic measurement error at the British Columbia sites caused by higher liquid water contents and more layers in the snowpack. The measured and the modeled SWE for Saskatchewan snowpacks with high liquid water contents were found to be weakly associated with correlations of 0Ð30. The acoustically-determined values of tortuosity were close to unity ( ˛ 1), which is in agreement with the values characteristic of snow as a porous substance. Further research is necessary to determine whether this technique can be applied to snow in
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.686 | 0.672 |
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".