Measuring snow accumulation and ablation dynamics during rain‐on‐snow events: innovative measurement techniques
Bibliographic record
Abstract
Abstract Rain‐on‐snow (ROS) is the primary generator of peak flow events in mountainous coastal regions of North America. Uncertainty remains as to the role of forest canopy interception leading up to and during ROS events. Much of this uncertainty can be attributed to a lack of suitable techniques to collect data during ROS, due in part to the dynamic nature of climatic conditions, particularly related to snow accumulation and melt. We supplemented a meteorological network with non‐weighing snow melt lysimeters, suspended spring scales to measure snow throughfall and an automated time lapse photography network to monitor state of precipitation (rain vs. snow), snow accumulation/ablation, canopy interception and unloading of snow from the canopy. Image analysis software allowed for the extraction of data from images. Rapid loading and unloading of snow from the canopy, closely linked to changes in temperature, was observed using this approach. We were also able to continuously monitor throughfall snow water equivalent using low cost suspended spring scales. This experimental design allowed us to capture information previously unavailable without direct observation. Copyright © 2008 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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".