Bathymetric Mapping of Shallow Water in Thaw Lakes on the North Slope of Alaska with Spaceborne Imaging Radar
Bibliographic record
Abstract
Few bathymetric maps are available for the thousands of thaw lakes on the North Slope of Alaska. We describe a semiautomated procedure for bathymetric mapping of water up to 2 m deep (i.e., less deep than the maximum ice thickness) in these lakes. A sequence of ERS-1 synthetic aperture radar (SAR) images and a simulated ice growth curve for winter 1991-92 are used to derive a digital elevation model of lake basins. The method is based on discriminating between floating ice and grounded ice in the SAR images to define raw isobaths; assigning an ice thickness or water depth to each isobath from the simulated ice-growth curve, and interpolating to create equally spaced (0.25 m) isobaths. There is modest agreement between SAR-derived maps and the few available bathymetric maps. Differences between the SAR maps and the original maps are probably unavoidable because of different production methods and original data formats. The concept of using SAR and a simulated ice-growth curve for bathymetric mapping of thaw lakes would benefit from verification based on a comparison with new maps derived from accurate field measurements at a selection of lakes with different morphological characteristics. Nevertheless, it is concluded that this technique is sound and could be used routinely for inexpensive and accurate bathymetric mapping across the entire North Slope and elsewhere (e.g., in Siberia, where large numbers of thaw lakes also occur). Such mapping would greatly increase the amount and spatial coverage of bathymetric data and would provide an accurate baseline against which to detect changes in the size, shape, bottom topography, and location of lakes.
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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 | 0.001 | 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) | 0.001 | 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".