Pan-Arctic 200m Resolution Ice Concentration Mapping by Fusing RCM, Sentinel-1 and AMSR2 Data via Deep Learning
Bibliographic record
Abstract
The second contribution of this research is the design of a geographically-weighted L1 loss function that better address the uncertainties in the reference IC data.The PM IC product, which is derived by using the NASA Team (NT) algorithm, is used as reference in this research.This NT algorithm is a widely used approach to generate coarseresolution IC product using AMSR2 data (Cavalieri et al., 1984).The NT product achieves better performance for open water region and ice region than the marginal ice zone (MIZ) region.Therefore, to leverage the spatially-varying nature of product uncertainty, we design a geographically-weighted loss function approach according to the U.S. National Ice Center daily ice charts, where we assign the highest weight to open water region, medium weight to the ice region, and lowest weight to the MIZ region.Furthermore, the L1 loss is used to calculate the discrepancies between 11 reference IC classes and the model output.The L1 loss is less sensitive to outliers/errors in the reference IC than the commonly adopted L2 loss, which reduces the effect of ambiguity on the model.The combined use of the geographically-weighted approach and the L1 loss leads to a new solution that can better address the uncertainties in the coarse-resolution NT
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".