Processes and Products of Turbidity Currents and Submarine Landslides in a Glacierized Fjord (Southwind Fjord, Baffin Island)
Bibliographic record
Abstract
Abstract Sediment transported to fjords is redistributed by turbidity currents and sometimes fails on steep sidewall slopes, forming marine geohazards that are known to impact infrastructure. Since marine geohazards are poorly understood in Arctic Fjords due to lack of data and monitoring, a comprehensive study of Southwind Fjord, Baffin Island, was undertaken to assess the modern processes leading to marine geohazards and their products on the seabed. Repeat measurements of bathymetric changes and flow measurements from moorings revealed that turbidity currents with measured speeds up to 1.75 ms −1 lead to the migration of cyclic steps in the submarine channel of the prodelta. Fast and dense heads of turbidity currents transport sand kilometers away from the channel‐mouth during larger events and remain confined in the prodelta channel and on the fjord basin floor. Clayey silts are deposited on the sidewalls of the fjord as a result of both overflowing turbidity currents and settling of meltwater plumes. Since sand is confined to the fjord bottom, there is no regional weak layer on the sidewall that is responsible for the large number of submarine landslides observed on the slopes. Low factor of safety of sidewall sediment (1.7 at 2.5 m depth) indicates that limited environmental loading of the sediment can trigger shallow (≤3 m) failures. This is confirmed by repeat bathymetric and core data showing asynchronous failures caused by icebergs and subaerial debris flows. This study provides a comprehensive overview of modern seabed processes and provides new perspectives on the wide variability of causes of marine geohazards in glacierized fjords that will be useful to interpret other similar environments with limited seafloor data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".