Investigations of newly formed sea ice in the Cape Bathurst polynya: 2. Microwave emission
Bibliographic record
Abstract
This study examines the role of newly formed sea ice geophysical state on microwave emission. Coincident with sea ice geophysical sampling, ship‐based passive microwave emission data (dual‐polarized at 19, 37 and 85 GHz) were collected in the Cape Bathurst Polynya during 18 October and 13 November 2003. Using polarization ratios (PRs), we found that bare thin ice was separable from snow‐covered ice. Thin snow (equal to 0.02–0.13 m) thickness is significantly correlated with the spectral gradient ratios GRV(85,19) (R2 = 0.55, P‐value <0.05) and GRV(85,37) (R2 = 0.66, P‐value < 0.05), but not with GRV(37,19) (R2 = 0.19, P‐value > 0.2). The relationship between atmospherically corrected R37 and bare ice thickness showed an exponential relationship very comparable to that reported by Martin et al. [2004] , which is ascribed to the reduction of bare ice surface salinity based on both observational and modeling studies. However, the relationship quickly becomes invalid for even thin snow covered ice, due to significant impact of thin wet (liquid water fraction ∼0.02–0.04) snow on microwave emission. Our results suggest that the sea ice algorithms NASA Team and NASA Team 2 could underestimate total ice concentration over thin bare ice by 35% on average, while both algorithms underestimate the total ice concentration by 20% over snow‐covered ice. Using PR(85) sea ice could be delineated from open water using a properly adjusted threshold value accounting for cloud or fog effects, possibly with the exception of dark nilas and/or bare consolidated pancakes.
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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 | 0.001 | 0.001 |
| 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".