Bibliographic record
Abstract
In this paper, an effective model is developed for retrieving sea ice thickness (SIT) from scattering coefficient (σ0) produced with TechDemoSat-1 (TDS-1) data. Here, σ0is formulated as the product of the propagation loss due to SIT and the reflection coefficient of underlying seawater. In application, σ0at specular point was firstly generated based on radar equation using TDS-1 data. Next, SIT was calculated from TDS-1 σ0using the proposed model, and verified with reference SIT data obtained by the Soil Moisture Ocean Salinity (SMOS) satellite. The data used here were from measurements over the year of 2015. Comparison results showed a good consistency between the derived and reference SIT, with a correlation coefficient of 0.90 and a root mean square difference of 8.68 cm, which demonstrates the potential of developed model and the utility of TDS-1 data for SIT retrieval.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".