Modeling VHF Scatter from FY and MY Sea Ice Ridges
Bibliographic record
Abstract
Declining sea ice conditions are often cited as an indicator of climate change, but limited information is available on sea ice ridges, which comprise up to 50% of ice volume in certain regions. Multi-year (MY) ridges are more hazardous barriers to navigation than first year (FY) ridges and they are more likely to survive the summer melt season, but little effort has been made to identify FY and MY ridges from remote sensing data. This paper describes an analytical method to model electromagnetic scattering from ridges around the VHF range (100-500 MHz) when ridges are modeled as a rough surface over stratified media. Models considering the macroscopic properties of FY and MY ridges relevant to VHF scatter are presented. Simulations show that FY and MY ridges have different scattering characteristics, making it possible to separate the two ice types. Future work will consider how variability in ice ridge characteristics and changes during the ice season affect the separability of FY and MY ridges based on their scattering characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".