Sea Ice Thickness Estimation From TechDemoSat-1 and Soil Moisture Ocean Salinity Data Using Machine Learning Methods
Bibliographic record
Abstract
In this paper, two machine learning methods, specifically, convolutional neural network (CNN) and support vector regression (SVR), are employed for retrieving sea ice thickness (SIT) from TechDemoSat-1 (TDS-1) and Soil Moisture Ocean Salinity (SMOS) data. The input for both methods consists of scattering coefficient (σ°), the incidence angle (θ), sea ice salinity (S) and sea ice temperature (T). The first two variables are derived from the TDS-1 data, and the latter two are from the SMOS data. Evaluation of the proposed methods is based on measurements in 2017 and 2018 of thin sea ice with thickness less than 1 m. Comparisons showed good consistency between the derived and reference SIT, with correlation coefficients of 0.95 and 0.90 and root mean square differences of 5.49 cm and 7.97 cm for SVR and CNN, respectively. This demonstrates the capability of these machine learning-based methods and the utility of TDS-1 data for SIT retrieval.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".