OBJECT-ORIENTED ANALYSIS OF SEA ICE FRAGMENTATION USING SAR IMAGERY TO DETERMINE PACIFIC WALRUS HABITAT
Bibliographic record
Abstract
Long-term alterations in climate are causing changes in sea ice formation resulting in a potentially degraded habitat for Pacific walrus (Odobenus rosmarus divergens). Students from NASA’s DEVELOP program worked with the U.S. Fish and Wildlife Service in Alaska to determine the usefulness of satellite imagery for studying walrus habitat on sea ice. Few studies use sea ice image processing methods to observe marine mammal habitats in polar regions because of the difficulty in obtaining multispectral imagery and georeferenced species location data points for the same time period. The dynamic nature of sea ice poses a challenge to remote sensing studies and matters are further complicated when additional data are incorporated. Passive multispectral sensors cannot penetrate the cloud base without information loss. In cases where heavy cloud cover exists, such as in the Alaskan Yukon-Kuskokwim Delta, radar sensors are preferred because they are relatively unaffected by clouds, have high temporal resolution, and operate day or night. This study presents a method for sea ice image analysis using remote sensing segmentation and classification techniques with RADARSAT1 Synthetic Aperture Radar. Results were associated with ground point data to determine the relationships of sea ice features to walrus’ preferred habitat. MODIS data were utilized, where possible, to verify the classifications of sea ice surfaces obtained by RADARSAT1. The challenge and goal was to capture, display, and relate geophysical information from radar images that correlate with georeferenced species data points for the same time period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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".