3D SAR Speckle Offset Tracking Potential for Monitoring Landfast Ice Growth and Displacement
Bibliographic record
Abstract
This study investigates the growth and displacement of landfast ice along the shoreline of the Mackenzie Delta in Northwest Territories, Canada, by synthetic aperture radar (SAR) speckle offset tracking (SPO). Three-dimensional (3D) offsets were reconstructed from Sentinel-1 ascending and descending SAR images acquired on the same dates during the November 2017–April 2018 and October 2018–May 2019 annual cycles. The analysis revealed both horizontal and vertical offsets. The annual horizontal offsets of up to ~8 m are interpreted as landfast ice displacements caused by wind and ocean currents. The annual vertical offsets of approximately −1 to −2 m were observed from landfast ice, which are likely due to longer radar penetration up to the ice–water interface with increasing landfast ice thickness. Numerical ice thickness model estimates supported the conclusion that the cumulative vertical negative offsets correspond to the growth of freshwater ice. Time-series analysis showed that the significant growth and displacement of landfast ice in the Mackenzie Delta occurred between November and January during the 2017–2018 and 2018–2019 cycles.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".