The influence of sensor overpass time on passive microwave derived snow water equivalent measurements
Bibliographic record
Abstract
Passive microwave derived retrieval of terrestrial snow water equivalent (SWE) is strongly influenced by snowpack wetness. The presence of water in the crystal matrix increases the microwave emissivity, destroying the between channel brightness temperature gradient used to make quantitative estimates of SWE. In order to obtain the most accurate SWE imagery, overpass times from orbiting sensors such as the Special Sensor Microwave/Imager (SSM/I) can be chosen so that the diurnally coldest and driest snowpack is being monitored. The authors evaluate the ro/spl circ/le of sensor overpass time when mapping SWE by comparing two datasets of five-day averaged, boreal winter season (December, January, and February) SWE imagery for a ground validated prairie study area. The first dataset is derived from SSM/I morning overpass times, the second from afternoon overpass times. Correlation analysis and modified mean bias error calculations are used to quantify the association between the two datasets. In addition, a series of principal components analysis tests quantify the spatial and temporal association between the two time series.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".