The effects of commercial vessel anchorages span ecological, cultural, and socio-economic endpoints
Bibliographic record
Abstract
Anchorages are specific areas used by vessels to maintain position and are used as waiting areas for freighters wanting to enter ports. The surge in demand experienced by ports from 2019 to 2022 significantly extended wait times at anchorages, heightening concerns of potential ecological and socio-economic effects among coastal communities. Effective anchorage management requires a connected and holistic approach to understand these diverse and complex effects. We summarise current knowledge on the cumulative effects of anchoring on ecological and socio-economic endpoints in a Pathways of Effects conceptual model informed by scientific literature and public consultation documents. We developed a Pathways of Effects Matrix (PoEM), a graphical advance designed to concisely visualise complex effects and explore mitigation scenarios, demonstrated in the example for commercial anchoring in Pacific Canada. In addition to supporting management decisions, this simple visual tool can also provide a way for communities to communicate their concerns in a structured way. • We developed a Pathways of Effects Matrix (PoEM) conceptual model for anchoring. • The anchoring PoEM details activities, stressors and their effects on endpoints • The graphical advance includes a range of ecological and socio-economic components. • Examination of the model can be used to evaluate mitigation and management measures.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".