Bibliographic record
Abstract
Abstract. In this study, a new method to assimilate satellite radar altimetry derived freeboard instead of sea ice thickness is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating freeboard, we compare three different model runs. One reference run (refRun), one that assimilates only SIC (sicRun) and one that assimilates both SIC and FB (fbRun). It is shown that, estimates for both SIC and FB can be improved by assimilation, but only the fbRun improved the sea ice thickness estimates. The resulting sea ice thickness is evaluated by comparing it to Alfred Wegener Institute's (AWI) weekly CryoSat-2 sea ice thickness data product, which is based on the same FB observations as were assimilated in this study. It is shown that the sea ice thickness from the fbRun is closer to the traditional CryoSat-2 sea ice thickness than sea ice thickness from refRun or sicRun. Additionally, we compare independent sea ice draft measurements from the Beaufort Gyre Exploration Project to both fbRun sea ice thickness and observed CryoSat-2 sea ice thickness. This comparison shows that our new method provides equally good results as the AWI weekly CryoSat-2 product; in two of three locations even better results.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.221 | 0.177 |
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".