A New Toolset for Multiscale Seabed Characterization
Bibliographic record
Abstract
Terrain attributes are increasingly used in seabed mapping to describe the shape of the seabed. In recent years, many calls have been made to move seabed mapping practices towards multiscale characterization to better capture the natural geomorphic patterns found at different spatial scales. However, the community of practice lacks computationally efficient, user-friendly, and open-source tools to implement multiscale analyses, preventing multiscale analyses from gaining traction for seabed mapping and characterization. Here we present a new R package that enables the calculation of multiple terrain attributes like slope, curvature, and rugosity from bathymetric data. The user-friendly package allows for a repeatable and well-documented workflow that can be run using open-source tools. We also introduce a new measure of rugosity that ensures decoupling from slope. Examples of the performance of the package, including the new rugosity metric, will be presented using bathymetric datasets presenting different characteristics.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".