Evaluation of satellite-based observations for capturing early winter snowmelt
Bibliographic record
Abstract
Over the past fifty years’ global climate change has altered various environmental processes. Due to global climate change, mid-winter snowmelt is occurring more frequently throughout much of the world (Freudiger, Kohn, Stahl, & Weiler, 2014). The increasing frequency of these events is a relatively new phenomena and is challenging the effectiveness of current water resource management and flood forecasting best practices. Early snowmelt events are caused by a brief period of unusually high air temperature, high humidity, or rain-on-snow (Semmens, Ramage, Bartsch, & Liston, 2013). This research focuses on the detection of rain-on-snow events using remote sensing approaches to identify the frequency, extent, and magnitude of these events. Early snowmelt events, driven by rainfall with the presence of snow, are identified from The Dartmouth Flood Observatory archives. Passive microwave data from the AMSR-E and SSM/I satellite instruments are compared with MODIS imagery and field observations to assess the reliability of microwave observations to capture these events. Early snowmelt detection algorithms that use passive microwave retrievals for northern latitude areas, primarily Alaska and Canada, are evaluated in the continental United States. It was determined that regional climate differences, largely variations in winter air temperature, impact the interpretation and performance of microwave snow melt detection algorithms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".