Investigating the Influence of Variable Freshwater Ice Types on Passive and Active Microwave Observations
Bibliographic record
Abstract
Dual-polarized airborne passive microwave (PM) brightness temperatures (Tb) at 6.9 GHz H/V, 19 GHz H/V and 37 GHz H/V and spaceborne active microwave (AM) X-band (9.65 GHz VV, VH) backscatter (σ0) are observed coincident to in situ snow and lake-ice measurements collected over two lakes near Inuvik, Canada. Lake-ice thickness is found to be positively correlated with 19 GHz V emission (R = 0.67) and negatively with 19 GHz H emission (R = −0.79), indicating surface ice conditions influence microwave interaction. Lake ice types are delineated from TerraSAR-X synthetic aperture radar (SAR) images using the iterative region growing with semantics (IRGS) segmentation algorithm implemented in the MAGIC (MAp Guided Ice Classification) system. The spatial extent of derived ice type classes correspond well to in situ observations. The overall magnitude of emission at 19 GHz H and X-band VH σ0 increase with the scattering potential of associated ice types (grey/rafted ice). Transects of 6.9 GHz PM and 19 GHz PM exhibit positive relationships with VH σ0 over freshwater lake ice, with the greatest R coefficients at H-pol (R = 0.64, 0.46). Conversely, 6.9 GHz Tb and 19 GHz Tb exhibit negative R coefficients in regions of brackish water due to tubular bubble and brine inclusions in the ice. This study identifies congruency between PM and AM scattering mechanisms over lake ice for the purpose of identifying the influence of ice types on overall microwave interaction within the lake-ice system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".