Morphometric fingerprinting of submarine canyon and channel processes revealed by time-lapse bathymetric surveys from the Congo Fan
Bibliographic record
Abstract
Submarine canyons and channels include the largest sediment transport systems on our planet. They are an important transport pathway for sediment, organic carbon, nutrients and pollutants to the deep sea. However, it is challenging to study these submarine locations, especially larger systems on the deep seafloor, and they remain poorly understood. Here we use the first extensive time-lapse bathymetric surveys of the Congo Submarine Fan (offshore West Africa), one of the largest submarine fans in the world. Channel-modifying processes (such as landslides, avulsions and knickpoints) are identified by comparing new high-resolution bathymetric data from 2019 to lower-resolution bathymetric data collected between 1992 and 1998, along a 475 km section of the Congo submarine system. These channel-modifying processes leave a specific fingerprint in morphometric characteristics (e.g., bed slope, width, cross-sectional flow area, sinuosity, levee slope and height) that are automatically extracted with a Matlab script from the bathymetric data. This work has the important implication that the identification of channel-modifying processes can be based on a single bathymetric survey, and does not require repeated surveys. In the upstream part of the Congo Canyon, a re-analysis of bathymetric data collected between 1992 and 1998 reveals a previously unnoticed channel-blocking landslide, which is of similar magnitude to a more recent landslide observed from the repeated surveys with a volume of ~0.4 km³. This observation of additional landslides supports the concept that the upstream canyon is morphologically defined by flank collapses, which can locally block the channel and store material for extended periods of time. In the intermediate channel part of the Congo Fan, avulsions already identified in previous work are demonstrated to leave a specific fingerprint within the morphometric characteristics such as a change in levee slope. In the most distal and youngest part of the Congo submarine channel, upstream migrating knickpoints are dominant and are shown to also leave a specific fingerprint in morphometric characteristics. These findings can underpin efficient searches for submarine canyon and channel processes in other systems, and provide new insights into how turbidity currents flush sediment into the deep-sea.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".