Mass wasting on Alpha Ridge in the Arctic Ocean: new insights from multibeam bathymetry and sub-bottom profiler data
Bibliographic record
Abstract
Abstract Marine geological and geophysical data from Alpha Ridge in the Arctic Ocean are sparse because of thick perennial sea-ice cover, which prevents access by most surface vessels. Rare seismic data in this area, acquired largely from drifting ice-camps, had shown the hemipelagic drape that covers most of the ridge is highly disrupted within a large (>90 000 km 2 ) south central region. Here, evidence of pronounced seafloor erosion and debris flows infilling seafloor lows was previously interpreted to be the result of a possible bolide impact. In recent years, several icebreaker expeditions have successfully acquired multibeam bathymetry and sub-bottom profiler data in the western segment of this region. Analysis of these data reveals a complex seafloor morphology characterized by ridges and troughs, angular blocks and escarpments as well as seismic facies characterized by hyperbolic seafloor reflections, and convoluted to incoherent and transparent sub-bottom reflectivity. These features are interpreted as evidence of sediment mass movement with varying degrees of lateral transport deformation. At least two episodes of failure are interpreted based on the presence of both buried and surficial mass-transport features. As multiple events are interpreted, seismicity is the most plausible trigger mechanism rather than bolide impact.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".