Bibliographic record
Abstract
Sea ice ridges pose some of the greatest threats to navigation in polar and sub-polar regions and it is important to characterize ridges using remote sensing data. First year (FY) ridges and multi-year (MY) ridges may be present in the same region, but MY ridges are stronger and have the potential to cause greater damage to vessels and structures. A new approach has been developed to model electromagnetic (EM) scattering from sea ice ridges. Ridges are represented as having a rough surface over horizontally stratified and isotropic layers. The total scatter from the ridge is the sum of the scatter from the surface and the layers. This paper provides an overview of the modeling process and provides some initial simulation results showing the impacts of radar frequency and ice salinity on the scattered wave. Ice ridge surface and structure characteristics affect the nature of the scattered signal and additional work is required to determine if it is possible to reliably distinguish between ridge types. Future work will involve simulations using more detailed modeling of ice ridge profiles to better determine how radar frequency and ice ridge structure and phenology affect ice discrimination.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".