Young sea ice signatures in the deep Arctic during the fall freeze-up
Bibliographic record
Abstract
The authors performed radar backscatter measurements early in the fall freeze-up as part of the 1991 International Arctic Ocean Expedition (IAOE'91) to investigate the ability of observing and distinguishing between young sea ice types with radar. Measurements were taken at all four linear polarizations (VV, HH, VH, HV) with a C-band FM-CW radar system built at The University of Kansas Radar Systems and Remote Sensing Laboratory (RSL). A number of young ice types were investigated during IAOE'91. These included light nilas, dark nilas, and pancake and slush sea ice types. Monitoring the early stages of sea ice growth is vital because the changing sea ice cover controls the heat exchange between the ocean and air. New ice growth is also responsible for adding brine into the upper portion of the water column. Thin sea ice also significantly affects the albedo of the surface as it changes from a sea surface to an ice surface. These radar measurements indicate that for spaceborne radar sensors such as the ERS-1 SAR and the Canadian RADARSAT SAR to be able to observe dark nilas ice they must be able to measure /spl sigmasup 0/ as low as -30 dB for VV polarization (ERS-1) and -34 dB for HH polarization (RADARSAT). The ERS-1 SAR should be able to observe slightly older sea ice types, such as light nilas and pancake ice, due to the higher backscatter from these ice types. To observe the cross-polarized response of these young ice formations a radar sensor must be capable of measuring /spl sigmasup 0/ values as low as -35 to -50 dB.>
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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 | 0.000 | 0.000 |
| 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".