Arctic Nearshore Sediment Dynamics from High-Resolution Phase Measuring Bathymetric Sonar Data: bathymetry, backscatter imagery, and seabed classification
Bibliographic record
Abstract
Carbon-rich permafrost coasts in the Arctic are subject to rapid erosion, causing vast quantities of carbon and sediments to enter the nearshore. According to many climate models, the Arctic will experience disproportionate warming in coming decades. One of the consequences will be increased coastal erosion, as the open water season lengthens. The release of carbon raises a number of questions ranging from the impact on local ecosystems, to potential climate change feedbacks. Due to the size and remoteness of arctic coasts, relatively few studies addressed the question of carbon transfer into the Arctic Ocean. These studies rely on remotely sensed data which means that the submarine portion of the coast, the processes and storage potential remain largely unknown. The Coastal Permafrost Erosion project by the Alfred Wegener Institute aims to answer some of these questions by quantifying erosion and characterizing eroded material for the whole coastal tract in the vicinity of Herschel Island, Yukon Territory, Canada. In this study, we describe the investigations of nearshore sediment and carbon dynamics carried out as part of the project in 2012 and 2013. The objective of the study is to establish a baseline dataset on bottom sediments, and gain insight into transport processes, pathways, and sinks. \n Phase Measuring Bathymetry allows to collect co-registered high-resolution bathymetry and side scan sonar data. The system chosen was a 500kHz system, which provided optimum coverage for the water depths of the reference area. The bathymetric data provided insight into physical processes such as ice gouging (Fig. 1), while compensated side scan mosaics delivered a map of sediment distribution (Fig. 2). The side scan data was processed, mosaicked and classified using the software tools provided by the sonar manufacturer, as well as the bathymetry. For the present study, both were gridded at 0.5 m resolution. \nThrough the compensated backscatter imagery, we identified areas where fine sediments are deposited and carbon may be stored. The classified seafloor image was validated by grain size parameters from the benthic grab samples with a very high degree of correlation. A significant portion of the released carbon is particulate organic matter which is transported along with the sediment. Elemental analysis of the grab samples revealed that organic carbon may account for up to 9% of the bottom sediment. We also found that the shoreface is subject to intensive ice gouging. Finally, we found that the sediment distribution in the nearshore is related to terrestrial features being eroded, e.g. cliffs, or valleys. \n
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".