Comparing SSM/I snow depth estimates to in-situ and interpolated multi-source measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spaceborne remote sensing data from the Special Sensor Microwave Imager (SSM/I) have been used for several decades to estimate snow depth over large regions. The SSM/I snow depth accuracy is not well quantified in non-uniform terrain. In this study, SSM/I snow depth estimates for the Columbia River Basin and surroundings in the Western USA and Canada are compared with in-situ manual snow-course measurements and interpolated snow water equivalent from the National Operational Hydrologic Remote Sensing Center. Snow depth is estimated for 25-km pixels from SSM/I brightness temperatures with the widely used Chang algorithm, adjusted for canopy cover. Interactive Data Language and ESRI ArcGIS are used to generate maps and time-series graphs, and to analyze the agreement between SSM/I snow depth and the other data sources. Measures of agreement are cross-tabulated with quantitative landscape descriptors, including: mean pixel elevation, elevation standard deviation (a measure of terrain complexity), and evergreen canopy cover.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it