Bottomfast Ice Mapping and the Measurement of Ice Thickness on Tundra Lakes Using C‐Band Synthetic Aperture Radar Remote Sensing<sup>1</sup>
Bibliographic record
Abstract
Abstract: Industrial activity in Canada’s north is increasing, placing demands on the use of water from lakes to build ice roads. Winter water withdrawal from these lakes has the potential to impact overwintering fish. Removal of water from small lakes can decrease oxygen and habitat available to fish. To address this issue, a protocol has been developed by the Department of Fisheries and Oceans outlining water withdrawal thresholds. Bathymetric surveys are the traditional method to determine lake depth, but are costly given the remoteness of northern lakes. This paper investigates the use of satellite C‐band synthetic aperture radar (SAR) remote sensing technology as a potential alternative or complement to traditional survey methods. Previous research has shown that a SAR can detect the transition from grounded to floating ice on lakes, or if a lake is completely frozen. Grounded ice has a dark signature while floating ice appears very bright in contrast. Similar results were observed for the datasets acquired in the study area. This suggests that lakes that freeze completely to the bottom can be identified using SAR. Such water bodies would not be viable fish overwintering habitat and can therefore be used as water sources without thresholds necessary. However, attempts to accurately calculate the depth of the ice at the grounded‐floating ice boundary using bathymetric profiles acquired in the summer and lake ice thickness measurements from a reference lake near Inuvik proved to be unreliable.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".