What controls submarine channel development and the morphology of deltas entering deep‐water fjords?
Bibliographic record
Abstract
Abstract River deltas and associated turbidity current systems produce some of the largest and most rapid sediment accumulations on our planet. These systems bury globally significant volumes of organic carbon and determine the runout distance of potentially hazardous sediment flows and the shape of their deposits. Here we seek to understand the main factors that determine the morphology of turbidity current systems linked to deltas in fjords, and why some locations have well developed submarine channels while others do not. Deltas and associated turbidity current systems are analysed initially in five fjord systems from British Columbia in Canada, and then more widely. This provides the basis for a general classification of delta and turbidity current system types, where rivers enter relatively deep (>200 m) water. Fjord‐delta area is found to be strongly bimodal. Avalanching of coarse‐grained bedload delivered by steep mountainous rivers produces small Gilbert‐type fan deltas, whose steep gradient (11°–25°) approaches the sediment's angle of repose. Bigger fjord‐head deltas are associated with much larger and finer‐grained rivers. These deltas have much lower gradients (1.5°–10°) that decrease offshore in a near exponential fashion. The lengths of turbidity current channels are highly variable, even in settings fed by rivers with similar discharges. This may be due to resetting of channel systems by delta‐top channel avulsions or major offshore landslides, as well as the amount and rate of sediment supplied to the delta front by rivers. © 2018 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".