Investigating the impacts of commercial anchorages on benthic ecosystems
Bibliographic record
Abstract
The expanding shipping industry has led to escalating use of commercial anchorages, with ships spending more time at anchorage and spreading to previously little used areas. Coastal communities have expressed concerns about anchorage stressors including visual pollution, noise, light, contaminant discharges, and seabed impacts. A recent Pathways of Effects conceptual model identified a range of possible effects of anchoring on physical habitats and marine biota but documented only a few scientific studies globally, with the focus on shallow recreational boat anchoring. Anchorages are often situated in soft sediment areas; understudied ecosystems with high diversity which play an important role in ecosystem function, and support commercial and ecologically significant species. Here we describe research to document the impacts of commercial shipping anchorages on marine benthic ecosystems of the Salish Sea and north coast of Pacific Canada. We undertook mapping of presumed anchoring-related marks observed in multibeam imagery and analysed data on anchorage activities, including age of anchorage, the intensity of use and the duration of anchoring events. We used the results to inform an initial Remotely Operated Vehicle (ROV) survey to examine visual evidence of the physical impacts of anchoring with field survey sites stratified across a range of seabed types and anchorages. The research investigates the mechanism behind seabed disturbance from anchorages in heavily trafficked areas, quantifies the extent of impact, and provides a baseline for change detection in these areas. Following this research, we aim to provide recommendations on considerations for siting and usage of anchorages for a more sustainable use of the seabed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".