Visual surveys provide baseline data on small vessel traffic and waterbirds in a coastal protected area
Bibliographic record
Abstract
The coastal waters of southern British Columbia, Canada, encompass habitat of international conservation significance to coastal and marine birds, including sizeable areas designated in the early 1900s as Migratory Bird Sanctuaries (MBS) to protect overwintering waterfowl from hunting near urban centres. Two of these, Shoal Harbour (SHMBS) and Victoria Harbour (VHMBS), have seen significant marine infrastructure development in recent decades and experience considerable vessel traffic. Vessel-related stressors are known to affect waterbirds, but traffic characteristics in coastal urban areas are poorly understood for the smaller vessels not tracked by Automatic Identification Systems (AIS). We conducted a pilot study using shore-based observers to develop small-vessel baselines for the winter months, when regional waterbird numbers are highest. During our surveys we recorded considerable inter-site variability in vessel traffic characteristics, with one site (SHMBS) a source of nearly twice as many vessel transits as the other (VHMBS). Most recorded vessels were small watercraft (mean length 26 ± 17', mode 18'), and vessels at the high-traffic site were both shorter and faster on average. One in six vessels were classified as 'noisy', of interest given that noise is an important component of vessel disturbance of waterbirds and other marine animals. Few vessels (7% of all recorded) were of the type required to carry AIS transponders, which highlights the monitoring gap created by using AIS-based approaches alone in nearshore waters, and allows for correction of AIS-derived vessel counts. Waterbird community composition also varied by locality, with one site dominated by gulls (Laridae), cormorants (Phalacrocoracidae), and seaducks (Tribe Mergini), and the other by gulls, cormorants, and alcids (Alcidae). Our results demonstrate that fine-scale local variability must be taken into account when managing for vessel traffic disturbance of waterbirds, particularly at sites of high human population density and increasing coastal development.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".