Mapping of Bottomfast Lake Ice in the Northwest Territories Via Data Mining of Synthetic Aperture Radar Image Time Series
Bibliographic record
Abstract
Changes in climate, warming temperatures and increased precipitation are impacting surface water resources in the Northwest Territories, Canada. Satellite remote sensing is an important tool to monitor variability in lake surface area, but monitoring depth is challenging. The distribution of bottomfast ice within a lake provides an indicator of depth and previous research shows that as lake ice develops and becomes bottomfast it exhibits a distinct signature when observed using multi-temporal Synthetic Aperture Radar (SAR) data. This research proposes an efficient computational technique for identifying bottom-fast ice across lakes in the Northwest Territories using multi-temporal SAR backscatter images and applies a function called dynamic time warping (DTW), which provides a shape-based similarity metric for time series data. We used backscatter profiles from surveyed lakes with known bottomfast ice to generate a DTW similarity metric on a pixel by pixel basis for a set of lakes. The similarity metric was used to categorize ice status as bottomfast or floating ice with 89.1% accuracy. DTW is an effective technique to map bottomfast ice using SAR time series and has potential to address limitations of other approaches where certain ice structures over deep lakes can produce backscatter responses similar to bottomfast ice.
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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 | 0.001 | 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".