Validating ICESat Over Thick Sea Ice in the Northern Canada Basin
Bibliographic record
Abstract
Only in the past eight years has the feasibility of using satellite-borne altimeters to estimate sea ice freeboard and thickness been demonstrated, and these estimates still have uncertainties primarily associated with limited knowledge of snow loading on sea ice. Because accurate estimates of Arctic-wide sea ice thickness and volume are fundamental inputs to global climate models, validation of satellite-derived thickness estimates using independent data is required. A detailed assessment of freeboard retrieved by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and land Elevation Satellite has been carried out using high-resolution laser altimetry from the National Aeronautics and Space Administration's Airborne Topographic Mapper (ATM), the Delay-Doppler radar altimeter, and digital photography collected along a 300-km segment of sea ice in the Canada Basin. Exploiting the repeat coverage of the aircraft flight line, a correction was applied to GLAS footprint geolocations to adjust for sea ice drift that occurred during the time between satellite and aircraft acquisitions. Comparisons of GLAS and ATM measurements over sea ice show excellent agreement (about a 0.00-m mean) with no apparent bias between data sets. Freeboard estimates were examined using data from GLAS and ATM independently, employing measurements over refrozen leads to estimate local sea surface heights (SSHs). The results demonstrate the sensitivity of freeboard and thickness calculations to an accurate estimation of local SSH. Snow depth derived by differencing laser and radar data was combined with the freeboard estimates to yield a mean sea ice thickness of ~ 5.5 m over a 250-km subsection of the flight track.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".