Inter-comparison and evaluation of sea ice type concentration algorithms
Bibliographic record
Abstract
Abstract. Sea ice has been monitored in terms of concentration and types with microwave satellite observations since the late 1970s. However, it remains an open question as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with two sea ice age (SIA) and three sea ice type (SIT) products using microwave radiometer and scatterometer data from 2000 to 2015. Their performances were evaluated quantitatively with samples that are used for generating tie points, and qualitatively with the RADARSAT imagery. The methods that combined scatterometer and radiometer data have overall better performances on ice type discrimination. The best methods are ECICE-QSCAT-2 for the years 2000–2009 and ECICE-ASCAT for 2009–2015, both using scatterometer data along with radiometer data. Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions, for instance, homogeneous sea ice regions with little artifact for SIA algorithms to 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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".