Using shore-based surveys to assess vessel traffic patterns in two migratory bird sanctuaries
Bibliographic record
Abstract
The waters of the Salish Sea encompass habitat of international conservation significance to coastal and marine birds, and include Shoal Harbour Migratory Bird Sanctuary and Victoria Harbour Migratory Bird Sanctuary (MBS; total area ~2000 ha). Both MBS were designated in the early 1900s to protect overwintering waterbirds from urban hunting, but have subsequently seen considerable development within their waters, including marinas, fuel docks, and other marine infrastructure. Vessel disturbances have been identified as a stressor to waterbirds, but traffic rates in these coastal areas are poorly understood for vessels without AIS tracking. We conducted a pilot study using shore-based observers to develop an MBS baseline of small and medium vessel traffic rates and characteristics for the winter months, when waterbird numbers are highest. Shore-based counts from two fixed points worked well to assess vessel traffic characteristics and record local waterbird numbers, and had the benefit of being low-cost to implement. Though surveys took place in winter when traffic volumes are lowest, counts of local vessel transits were sometimes high (max. 137 vessels over a 7-hour day at one site). Local traffic characteristics varied significantly by study site, with vessels at one being smaller, faster and more numerous than at the other. Very few vessels (7% of those recorded) were of the type required to carry AIS transceivers. Although an unknown proportion of small vessels uses AIS voluntarily, our study measured vessel traffic of the type not captured by automated approaches. This pilot study is a first step in identifying impacts of small vessel traffic on coastal waterbirds in the Canadian portion of the Salish 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".