Impact of ice temperature on microwave emissivity of thin newly formed sea ice
Bibliographic record
Abstract
This study examines the impact of ice temperature on microwave emissivity over thin, newly formed sea ice at 6, 19, and 37 GHz during October 2003 in the southern Beaufort Sea, where the physical properties of newly formed sea ice were coincidently measured with microwave emissions. Six ice stations with distinct properties were selected and divided according to ice surface temperature into warm (above –3°C) or cold (below –3°C) stations. The warm stations had a lower emissivity at the vertical polarization by 0.1 than the cold stations and a corresponding difference in brine volume and dielectric properties. Significant correlations were observed between brine volume and ice emissivity (R2 = 0.8, p value < 0.05). A sensitivity study showed that decreasing ice temperatures from −2.1° to −5.0°C explained the observed difference of 0.1 in ice emissivity between warm and cold stations. The results suggest that the temperature of thin bare ice could be the critical factor in determining ice emissivity near the melting point (about −2°C). Furthermore, a slight decrease in ice temperature (i.e., from −2° to −5°C) significantly reduces the brine volume, thus resulting in high ice emissivity. Finally, we demonstrate the potential of newly formed ice to cause errors in estimating sea ice concentrations using Advanced Microwave Scanning Radiometer–E data.
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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) | 0.000 | 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".