Slope failures along the deformation front of the Cascadia margin: linking slide morphology to subduction zone parameters
Bibliographic record
Abstract
Abstract Marine acoustic data are used to map and characterize submarine slope failure along the accretionary prism of Cascadia. Two main styles of slope failure are identified: (1) failures with curved head scarps, which are predominantly associated with incoherent debris-flow deposits; and (2) failures with rectangular head scarps, which are predominantly associated with intact sediment blocks. Rectangular head scarps mostly occur on thrust ridges with slope angles <16° and ridge heights <650 m, whereas curved head scarps occur predominantly on steeper and higher ridges. Off Vancouver Island, failure style and head-scarp geometry also change with ridge azimuth. We propose that the curved head scarps and debris flows may be a result of higher kinetic forcing of the downsliding sediments and a higher degree of mixing. At the more gently sloped, less elevated ridges, the kinetic forcing may be smaller, which leads to intact failure masses. Extensional faults at ridges with curved scarps may result from oversteepening and collapse of the sediments that cannot withstand their own weight due to limited internal shear strength. The slide geometries and potential controls on failure style may inform subsequent studies in assessing the risks for tsunami generation from submarine slope failures along the Cascadia margin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".