Microwave Emission Observations from Artificial Thin Sea Ice: The Ice-Tank Experiment
Bibliographic record
Abstract
Simulated sea ice was grown in an outdoor tank during the early winter seasons of 2001/2002 and 2005/2006. Microwave radiation was sampled every 5 min from the following three channels: 19, 37, and 85 GHz. Surface physical conditions were measured or observed to help in the interpretation of the radiometric behavior. This paper reports on results related to the following objectives: 1) linking the observed radiation to surface properties and processes; 2) classifying thin ice into emissivity-based surface types, and 3) assessing thin-ice parameter retrieval algorithms. This paper shows that ice of less than 4-cm thickness exhibits cycles of a sharp decrease of microwave emission caused by surface wetness followed by a gradual increase as the surface refreezes. This ice is particularly linked to meteorological conditions. Snow accumulation on relatively thick ice (> 20 cm) affects only the radiation from the 85-GHz channel. Thin-ice surfaces can be grouped into two radiometrically distinguished categories - the first includes slushy and wet surfaces and the second includes wet snow, dry snow, and dry bare-ice surfaces. Radiation from the second category is higher. The radiation from a refrozen slush surface appears to fall between these two categories. The variability of emissivity increases as the radiation frequency increases, particularly for the horizontal polarization channels. Existing algorithms of ice thickness, snow depth, and ice concentration were examined against the current data to study their sensitivity to variations of surface conditions. Limitations on their applications have been established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 | 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 teacher head, 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".