Predicting turbidity current activity offshore from meltwater-fed river deltas
Bibliographic record
Abstract
Quantification of the controls on turbidity current recurrence is required to better constrain land to sea fluxes of sediment, carbon and pollutants, and design resilient infrastructure that is vulnerable to such flows. This is particularly important offshore from river deltas, where sediment supply is high. Numerous mechanisms can trigger turbidity currents, even at a single river mouth. However quantitative analysis of recurrence and triggers has been limited to an individual trigger for each turbidity current due to the low number of precisely timed (via direct monitoring) flows. We are therefore yet to quantify if and how coincident processes combine to generate turbidity currents, and their relative importance. Here, we analyse the timing and causes of 113 turbidity currents directly-monitored from the source of turbidity current initiation to depositional sink in a single submarine channel. This submarine channel is located offshore from glacial-fed river-deltas at Bute Inlet, a fjord in British Columbia, Canada. Using a multivariate statistical approach, we demonstrate the statistical significance of combined river discharge and tidal controls on turbidity current occurrence during 2018, from which we derive a statistical model that calculates turbidity current probability for any given input of river discharge and water level. This new model predicts turbidity current activity with >84% success offshore other river deltas where flow timing is precisely constrained by directly monitoring, including the Squamish and Fraser River-deltas in British Columbia. We suggest that this model will be applicable for turbidity current prediction at glacial meltwater-fed fjords in many other regions worldwide.
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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 | 0.000 |
| 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) | 0.001 | 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".