A framework for coupling thermodynamic and backscatter models toward the estimation of Arctic sea ice, snow on sea ice, and snow brine volume
Bibliographic record
Abstract
We propose a modular framework for coupling sea ice thermodynamic models with microwave backscatter models for comparison to observations, with the objective of accurate accounting for snow and sea ice and associated dynamics over vast and remote areas of the Arctic Ocean. The framework uses seasonal, high resolution, in situ spatiotemporal data, including meteorological and surface-based polarimetric C-band scatterometer observations, collected during the CASES overwintering in first-year sea ice in Franklin Bay, NWT, in 2003 and 2004. The meteorological data drives SNTHERM and SNOWPACK sea ice simulations. The geophysical results of these thermodynamic models then drive multilayer snow and ice backscatter simulations for comparison to surface-based C-band microwave scatterometer observations. Brine profiles and snow-grain size are used to iteratively fine tune the simulation process to match backscatter simulations to observations for remote estimation of snow and sea ice geophysical properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".