TemporalTemp: Encoding Historical Temperatures for Automated Sea Ice Mapping
Bibliographic record
Abstract
This paper investigates the impact of incorporating historical ERA5 reanalysis data into deep learning models for Arctic sea ice parameter prediction. While existing approaches primarily rely on contemporaneous observations, we explore whether encoding temporal variations in ERA5 2-meter air temperature (t2m) can improve classification of sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). Using a multi-task U-Net architecture, we evaluate different temporal encoding strategies on the AI4Arctic dataset, comparing performance against a baseline model without historical context. Our results show that a 1-year encoding with 4 samples of 90 days each achieves the best overall performance (85.62% combined score), improving SOD and FLOE classification by 1.43% and 1.20% respectively. Qualitative analysis reveals that temperature encoding helps distinguish ice types during transitional seasons but can degrade SIC estimation during melt conditions when air temperatures become misleading indicators. These findings demonstrate that carefully designed temporal encodings can enhance sea ice classification while highlighting the need for complementary data sources to address limitations during melt periods.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".