Mass transport deposits in reflection seismic data offshore Oregon, USA
Bibliographic record
Abstract
Abstract Submarine landslides associated with the Cascadia subduction zone offshore of the Pacific northwest United States and Canada represent significant natural geohazards. Mapping past submarine landslide deposits is critical for understanding present and future slope failure and tsunami hazard potential. We focus on the portion of Cascadia offshore Oregon to map the occurrences of submarine landslide deposits (mass transport deposits [MTDs]) in the subsurface using recent high‐resolution reflection seismic data. We identified 133 MTDs and categorized them based on their present morphology inferred from their acoustic characteristics as disintegrative or blocky. Interestingly, nearly 76% of the MTDs are located in the northern Oregon margin and many of these are non‐cohesive disintegrative deposits. MTDs are less common in the southern Oregon margin, however, they were also much larger and more cohesive than those found in the north. The differences are not likely to be related to differences in earthquake intensity but rather sedimentation rates and basin structures. Specifically, the northern Oregon margin is proximal to the sediment‐delivery systems of the Columbia River and has landward verging fold‐and‐thrust structures, whereas the southern Oregon margin is relatively sediment starved and has seaward verging structures resulting in fewer steep ridges. Because of the higher sedimentation rates, the northern Oregon margin may be prone to more frequent and disintegrative types of slope failures. In contrast, the southern margin may have enhanced slope stability due to seismic strengthening and lower sedimentation rates. However, when slope failures do occur in the southern Oregon margin, they tend to be more cohesive and blocky. Therefore, even though there are fewer slope failures in the southern Oregon margin, there is still hazard potential because fast‐moving cohesive slope failures can generate tsunami.
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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".