The Sediment Budget Estimator (SBE): a process-model for the stochastic estimation of fluxes and budgets of sediment through submarine channel systems.
Bibliographic record
Abstract
Turbidity currents transport vast amounts of sediment through submarine channels onto deep-marine basin floor fans. There is a lack of quantitative tools for the reconstruction of the sediment budget of these systems. The aim of this paper is to construct a simple and user-friendly model that can estimate turbidity-current structure and sediment budget based on observable submarine channel dimensions and general characteristics of the system of interest. The requirements for the model were defined in the spirit of the source-to-sink perspective of sediment volume modeling: a simple, quantitative model that reflects natural variability and can be applied to ancient systems with sparse data-availability. The model uses the input conditions to parameterize analytical formulations for the velocity and concentration profiles of turbidity currents. Channel cross-section and temporal punctuation of turbidity-current activity in the channel are used to estimate sediment flux and sediment budget. The inherent uncertainties of geological sediment budget estimations motivate a stochastic approach, which results in histograms of sediment budget estimations, rather than discrete values. The model is validated against small-scale experimental turbidity currents and the 1929 Grand Banks turbidity current. It is found to perform within acceptable margins of error for sediment flux predictions at these smallest and largest scales of turbidity currents possible on Earth. This success motivates application of the model to a reconstruction of the sediment budget related to Cretaceous slope-channel deposits (Tres Pasos Formation, Chile). The results give insight into the likely highly stratified concentration profile and the flow velocity of the Cretaceous turbidity currents that formed the deposits. They also yield estimates of the typical volume of sediment transported through the channels while they were active. These volumes are demonstrated to vary greatly depending on the geologic interpretation of the relation between observable deposit geometries and the dimensions of the flows that formed them. Finally, the shape of the probability density functions of predicted sediment budgets is shown to depend on the geological (un)certainty ranges. Correct geological interpretations of deep marine deposits are therefore indispensable for quantifications of sediment budgets in deep marine systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".