A consistent global approach for the morphometric characterization of subaqueous landslides
Bibliographic record
Abstract
Abstract Landslides are common in aquatic settings worldwide, from lakes and coastal environments to the deep sea. Fast-moving, large-volume landslides can potentially trigger destructive tsunamis. Landslides damage and disrupt global communication links and other critical marine infrastructure. Landslide deposits act as foci for localized, but important, deep-seafloor biological communities. Under burial, landslide deposits play an important role in a successful petroleum system. While the broad importance of understanding subaqueous landslide processes is evident, a number of important scientific questions have yet to receive the needed attention. Collecting quantitative data is a critical step to addressing questions surrounding subaqueous landslides. Quantitative metrics of subaqueous landslides are routinely recorded, but which ones, and how they are defined, depends on the end-user focus. Differences in focus can inhibit communication of knowledge between communities, and complicate comparative analysis. This study outlines an approach specifically for consistent measurement of subaqueous landslide morphometrics to be used in the design of a broader, global open-source, peer-curated database. Examples from different settings illustrate how the approach can be applied, as well as the difficulties encountered when analysing different landslides and data types. Standardizing data collection for subaqueous landslides should result in more accurate geohazard predictions and resource estimation.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".