Observing sea ice ridges and deformed sea ice from satellites in the Arctic
Bibliographic record
Abstract
The objective of the study has been to assess capabilities and limitations of new earth observation satellites to detect and quantify sea ice ridges and other deformed sea ice types in the Arctic. Ridging and corresponding ice keels represent the thickest part of the sea ice cover. Detection and monitoring of ridges is therefore an important part of met-ice-ocean services to support operations in ice-covered seas. On large scale ridges can be observed by laser and radar altimeter data through a surface roughness parameter that is defined by standard deviation of the surface elevation measurements along the satellite orbit. The ICESat laser altimeter has provided experimental data over six weeks periods from 2003 to 2006. These data show clearly that the area north of Greenland and the Canadian Archipelago has the highest surface roughness of the Arctic sea ice and that firstyear ice has lower roughness than multiyear ice. This is in agreement with in situ observations and with aircraft laser measurements. On regional and local scale, satellite Synthetic Aperture Radar (SAR) images have been used to develop ridge detection methods over the last 10 – 15 years. Several studies of sea ice processes have been conducted in the Baltic Sea, the Barents Sea and Svalbard area, in the Russian Arctic and in Canadian waters, showing that SAR can be a useful tool to detect ridges and deformed ice. The advantage of the SAR is that it can be used to discriminate areas of deformed ice from areas of level ice. Also areas with hummocks, stamukhas and individual ridges of a certain width can be identified in SAR images. SAR image cannot provide any quantitative estimate of the height of ridges. This parameter can best be measured by laser altimeter. Use of SAR images with alternating polarization and high spatial resolution (better than 10 m) is expected to improve the classification of rough ice and detection of ridges. When SAR is used in combination with laser measurements from satellite or aircraft, the detection of ridges and leads is more reliable, reducing the ambiguity of the ridge signals in SAR images. Optical images can provide detailed maps of ice ridges if the resolution is very high, such as video records from aircraft.
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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 | 0.001 | 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".