The application of C-band and L-band polarimetric microwave radars in cryosphere (terrestrial snowfalls and oil spills within freezing seawater)
Bibliographic record
Abstract
The recent global warming and climate change have significantly altered the extents of sea ice and snow. Declining thickness of the Arctic sea ice increases maritime activities for resource extraction, refueling communities, tourism, and shipping. Changes of snowpack threaten water availability for agriculture, drinking water, and hydropower in dependent regions. Thus, remote sensing approach is essential for detecting and monitoring snowfall and oil-contaminated sea ice. This thesis focuses on studying two main objectives: 1) monitoring oil spills within freezing seawater (by aiming to understand the potential impacts of diesel fuel and wind on the growth, thermophysical, and C-band backscattering responses of newly forming sea ice.), and 2) investigating terrestrial snowfall events (by aiming to examine the capabilities of C-band and L-band scatterometers in detecting the presence of dry and wet snow, along with monitoring the thermophysical changes within the snowpacks). In order to investigate the objectives, three experiments were conducted during 2022-23 at the University of Manitoba research facilities (SERF and The Point). The experiments were centered on change detection, dual-frequency, and multi-scenario approaches to provide intercomparable results and interpretations. By accomplishing these objectives, this thesis makes a substantial contribution in developing both contemporary and future C-band and L-band satellites missions. It provides essential data, such as the location and extent of the snow on the ground, as well as the oil-contaminated sea ice in the Arctic for modeling and mapping programs. Consequently, the outcomes will provide successful supports to many climate change counter-responses and strategies around the world.
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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 | 0.000 | 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".