The use of RADARSAT-2 and TerraSAR-X data for the evaluation of snow characteristics in subarctic regions
Bibliographic record
Abstract
This paper investigates the potential for the combination of RADARSAT-2 and TerraSAR-X data to evaluate snow characteristics in subarctic regions. The study area situated around the Umiujaq community (56.55° N, 76.55° W) in northern Quebec, Canada. RADARSAT-2 and TerraSAR-X data were acquired between March 2010 and April 2012 during the fall and winter seasons. Snow measurements were made in coordination with satellite acquisitions and vegetation was sampled in the summer of 2009. A temporal analysis is first performed on the fall data to determine when ground freeze-up occurs. The fall image which corresponds to frozen conditions is then compared to winter images using temporal backscattering ratios. This method shows a good sensitivity to varying snow conditions and the different frequencies provide complementary information. However, there is still some ambiguity on the exact influence that shrub vegetation has on the SAR signal.
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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 | 0.002 | 0.002 |
| 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".