Satellite image processing in the circumpolar north: Understanding climate crisis by predicting sea ice extent in the arctic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it