Validation of daily snow water equivalent for a watershed statistics tool
Bibliographic record
Abstract
The Watershed Statistics tool is a tool currently being developed for the Remote Sensing and Geographic Information Systems Center of Expertise’s (RSGIS) Extreme Cold Weather web portal and will allow users to easily access and visualize snow water equivalent (SWE) data. The SWE data available on this tool are derived from passive microwave signals acquired by satellite through a technique known as enhanced passive microwave SWE. This analysis used available in situ SWE measurements from snow study sites in four watersheds across the United States and Canada to determine the accuracy of the data available on the tool at the watershed scale. In situ measurements of SWE were compared with the Watershed Statistics tool’s SWE data based on watershed, land cover, and elevation to determine causes if discrepancies between the satellite-based estimations on the tool and ground-based measurements. The extent to which the data available on the Watershed Statistics tool agreed with in situ measurements was highly variable. SWE data available on the Watershed Statistics tool agreed the least with ground-based measurements made at higher elevations and in areas with denser vegetation. The findings of this comparison are consistent with known limitations of the enhanced passive microwave SWE technique.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".