Comparison of the SnowHydro snow sampler with existing snow tube designs
Bibliographic record
Abstract
Abstract Snow tube samplers are the primary method of measuring snow water equivalent (SWE) in the field, as they are considerably less destructive to the snowpack and faster to use than traditional snow pit techniques. This study evaluates the performance of three commonly used snow tube designs: Standard Federal, Meteorological Service of Canada (MSC), and SnowHydro. The Standard Federal and MSC designs have previously been extensively tested; however, the SnowHydro is a new design for which an error analysis has not yet been published. We compared the three designs in a shallow, highly stratified snowpack in both a forest and a clearcut, conditions that are not well represented in previous studies. While the Standard Federal produced SWE values closest to snow pit measurements, the SnowHydro snow tube outperformed the other two designs in terms of coring performance and produced more consistent SWE measurements. Although this is the first published study to quantify the performance of the SnowHydro sampler, additional studies under varying snow conditions are required to adequately quantify sampling errors. Copyright © 2012 John Wiley & Sons, Ltd.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".