Character, spatial and temporal variation of turbidity currents on a source-to-sink scale.
Bibliographic record
Abstract
<p>Seafloor avalanches of sediment called turbidity currents are one of the principle mechanisms for moving sediments across our planet. However, turbidity currents are notoriously difficult to monitor directly in action, and we still mainly depend on their sedimentary deposits as well as physical and numerical models to understand their temporal and spatial evolution. In recent years, multiple studies have successfully made direct measurements within active turbidity currents at multiple sites along their pathway. However, these direct measurements are often limited to the upper reaches of submarine systems, only cover relatively short (few months to a couple of years) time scales, or have very few measurement stations (<3). To capture the full range of turbidity current types and recurrence times we need to combine direct monitoring with longer-term archives in sedimentary deposits. Here we present an unusual data set that extends from the submarine channel on the delta, to the final deposits in the deep basin. The dataset combines short-term (< 1 year) direct measurements of flows with long-term sediment deposits (dating back to about 100 years). This combination of data types allows us to understand turbidity current frequencies, runouts, heights and characteristics along an entire submarine system.  </p><p>We analyse data from Bute Inlet, which is a fjord in British Colombia, Canada. The entire turbidity current system stretches out for 80 km, with an incised submarine channel extending for 45 km. 46 Cores have been collected between 2015 and 2018. Simultaneously, direct measurements of the currents have been obtained in 2016 and 2018 using Acoustic Doppler Current Profilers (ADCPs) in the submarine channel.</p><p>Our objective is two-fold. First, we look at flow frequency over time and space. Visual logs of the sediment cores, as well as sediment accumulation rates for a selection of cores, are used to infer flow frequencies. We then use the ADCP data to understand more frequent and recent flows at 6 places along the channel. These ADCP measurements are used to infer frequencies which are not necessarily recorded in the deposits, and give additional insights into current-day activity. This allows us to reconstruct the change in frequency over space and time.</p><p>Second, we consider the variation in turbidity current character to understand how flows evolve along the channel. Facies determination and grain size data are used to infer turbidity current character. Cores along the channel, on terraces and in the deep basin are used to understand the spatial variation. Finally, comparison of deposits and monitoring (ADCP) data shows how submarine flows are recorded by their deposits.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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 teacher head, 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".