Satellite image processing in the circumpolar north: Understanding climate crisis by predicting sea ice extent in the arctic
Bibliographic record
Abstract
Observing and analyzing the changing polar ice patterns is crucial for understanding the climate crisis. Research works across the Circumpolar North use machine learning models to study and predict changes in sea ice. In this paper, we propose a deep learning model using satellite images of the Arctic, captured daily and monthly over a half-century period and curated at the National Snow and Ice Data Center (NSIDC), to forecast future ice extent. We perform a time-series analysis using a multimodal approach, combining a gated recurrent unit (GRU) with a transformer-based model to predict changes in Arctic ice. Our model explains approximately 92.02% of the variance in the true ice extent time series. The error metrics were low: We observed the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to be, respectively, 0.1362 and 0.1637. Preliminary assessments of our prototype show promising results in understanding past trends and making predictions.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".