Thin Sea Ice Thickness Prediction Using Multivariate Radar-Physical Features and Machine Learning Algorithms
Bibliographic record
Abstract
Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".