Seasonal characterization of microwave emissions from snow‐covered first‐year sea ice
Bibliographic record
Abstract
Abstract Brightness temperature T B data were collected with a surface‐based radiometer operating on both vertical and horizontal polarizations at frequencies of 19, 37, and 85 GHz. Both microwave emissions and thermophysical data were collected as part of the Collaborative–Interdisciplinary Cryospheric Experiment between 15 May and 25 June 2000, in the Canadian High Arctic. Each season was characterized by a running variance of the time series in the microwave emissions. The seasonal analysis was conducted through observed changes in the physical characteristics of the sea ice and the overlying snow pack. Results from a k ‐means clustering analysis show that variability in the microwave response can be categorized into phenomenological states that were described by Livingstone et al . [ IEEE Transactions on Geoscience and Remote Sensing 1987; 25 (2): 174–187] as winter, early melt, melt onset and advanced melt . We describe the average thermophysical conditions associated with each one of these ‘ablation states’ and interpret the relative contributions of each to the observed microwave response. Emissivities were calculated and used as part of a descriptive analysis of the seasonal variation of T B . Our results confirm other findings that the strength and pattern of the relationship are frequency dependent and relative to snow and ice dielectric properties. Useful information on the thermodynamic state of the snow–sea‐ice system can be derived from passive microwave data, since the microwave emissions respond to the general seasonal changes associated with the transition from winter to a melt ponded sea ice surface. Copyright © 2001 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.005 | 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".