A GIS Approach to Quantitative Ice Gouge Depth Mapping, Analysis, and Prediction
Bibliographic record
Abstract
Abstract We describe and illustrate the application of a geographical information system (GIS) approach to map ice gouge locations and depths from high-resolution multibeam echo sounder (MBES) bathymetric surfaces by calculating residuals relative to spatially variable moving trend surfaces. The workflow can be used to rapidly characterize gouges over large areas and, because minimal human intervention is required, is especially attractive in heavily gouged areas where traditional manual measurement techniques would be tedious and produce highly uncertain results. The method produces maps showing gouge depth as a continuous field rather than point measurements or cross-gouge profiles, so that variations in depth along gouges can be easily visualized and analyzed. Once gouges have been delineated, gouge depth distribution statistics can be further used to estimate exceedance probabilities for gouge depths within local neighborhoods. Seafloor roughness maps can also be generated to highlight the spatial variability of seafloor disturbance and, in a relative sense, visualize the ages of different gouges if certain assumptions are satisfied. We illustrate application of the method using a sample MBES data set depicting a heavily gouged portion of seafloor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".