Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay
Bibliographic record
Abstract
Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".