Assessment of Seasonal Sea Ice Type and Roughness Regime Discrimination Using a Unique C- and L-Band SAR Database
Bibliographic record
Abstract
To date some studies have outlined the benefits of low-frequency L-band synthetic aperture radar, compared to C-band and higher frequencies, for discriminating Arctic sea ice types during melting conditions. This is due to the higher penetration depth of L-band energy through wet snow and ice, yielding radar backscatter information more closely related to internal ice structure variations. In this study we utilize a database of seasonally varying C- and L-band SAR radar backscatter coefficients and polarimetric parameters from RADARSAT-2 and ALOS-2/PALSAR-2, and field measured variables including airborne measured ice thickness and roughness, to detail first-year and multiyear sea ice type discrimination capabilities during sub-stages within the seasonal melting period. Included in the evaluation is sea ice information for twenty C-band compact polarimetry (CP) parameters, in anticipation of the launch of the RADARSAT Constellation Mission (RCM). CP parameters were simulated from the fully polarimetric data collected from RADARSAT-2.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 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 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".