Comparison of thin ice thickness distributions derived from RADARSAT Geophysical Processor System and advanced very high resolution radiometer data sets
Bibliographic record
Abstract
Thin ice thickness distributions estimated from advanced very high resolution radiometer (AVHRR) and RADARSAT Geophysical Processor System (RGPS) data sets were compared over the Beaufort Sea and the Canada Basin for the period December 1996 to February 1997. The comparisons show a compelling agreement. High correlations were found in cases where thin ice grew in large, wide leads extending several hundred kilometers. At these large scales, estimates from AVHRR images and RGPS showed similar amounts of thin ice in leads. However, when major surface deformation occurred on small scales (100 m to 10 km), the finer spatial resolution (100 m) of RADARSAT images enabled the RGPS algorithm to derive more thin ice than that of AVHRR. Under such conditions the correlation between the two dropped, and a small negative bias (about 1%) was observed in the estimates from AVHRR. This bias, mostly concentrated at the very thin end of the thickness distribution, caused a further deficit in the AVHRR‐derived thin ice growth, roughly 0.2 cm/d. Although the AVHRR and RGPS algorithms treat snowfall differently in the ice thickness calculations, both snow assumptions appear reasonable. However, RGPS may underestimate the thin ice production because of the 3‐day sampling interval. With a better understanding of the sources of uncertainty and improved satellite estimates, a combination of these two satellite data could offer a wider coverage of thin ice thickness observations in the Arctic Basin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".