Radar Scatter Decomposition to Differentiate between Running Ice Accumulations and Intact Ice Covers along Rivers
Bibliographic record
Abstract
For ice-jam flood forecasting it is important to differentiate between intact ice covers and ice runs. Ice runs consist of long accumulations of rubble ice that stem from broken up ice covers or ice-jams that have released. A water wave generally travels ahead of the ice run at a faster celerity, arriving at the potentially high flood–risk area much sooner than the ice accumulation. Hence, a rapid detection of the ice run is necessary to lengthen response times for flood mitigation. Intact ice covers are stationary and hence are not an immediate threat to a downstream flood situation, allowing more time for flood preparedness. However, once ice accumulations are moving and potentially pose imminent impacts to flooding, flood response may have to switch from a mitigation to an evacuation mode of the flood management plan. Ice runs are generally observed, often by chance, through ground observations or airborne surveys. In this technical note, we introduce a novel method of differentiating ice runs from intact ice covers using imagery acquired from space-borne radar backscatter signals. The signals are decomposed into different scatter components—surface scattering, volume scattering and double-bounce—the ratios of one to another allow differentiation between intact and running ice. The method is demonstrated for the breakup season of spring 2018 along the Athabasca River, when an ice run shoved into an intact ice cover which led to some flooding in Fort McMurray, Alberta, Canada.
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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 | 0.000 | 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 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".