Evaluating methods to reduce humpback whale (Megaptera novaeamgliae) ship strike mortality in nearshore feeding habitats within the San Francisco Bay
Bibliographic record
Abstract
Across our world’s oceans, whales are perpetually stuck and killed by large ships. Sea-based commercial trade is increasing on a global scale; therefore, more large ships are circumnavigating through biologically important whale habitats. This is of grave concern to marine resource managers, especially in the United States where major shipping corridors overlap critical whale habitat. It is feared that in these areas with high whale density, ship strikes are reducing populations of whales to levels below what is sustainable for threatened and endangered species, thereby hindering the recovery of depleted whale populations. Humpback whales, a species that is federally-listed as endangered, congregate and feed within the National Marine Sanctuary system off the coast of San Francisco and these whales are increasingly prone to the threat of a lethal ship strike. Humpback dietary preferences shift between krill and schooling fish, and within the past five years, spawning northern anchovy have attracted increased numbers of humpback whales to nearshore waters heavily trafficked by large ships en route to ports within the San Francisco Bay. A voluntary speed reduction program implemented in the National Marine Sanctuaries has been partially effective at engaging commercial shipping operators towards compliance with the voluntary speed reductions. However, it is unclear if these strategies can be feasibly extended to waters within the San Francisco Bay, which is outside the jurisdictional boundaries of the sanctuaries. Additionally, compliance with the speed reduction measures needs to increase in order to adequately protect whales in this entire region. I evaluated voluntary ship strike reduction techniques that have been implemented in other international shipping ports with whales (Hauraki Bay, New Zealand and St. Lawrence Estuary, Quebec, Canada), and interviewed local subject matter experts to determine which aspects of voluntary speed reduction measures were the primary drivers of increased voluntary speed reduction compliance, and vi which of those can feasibly be implemented in the San Francisco Bay to protect humpbacks. Educational outreach and onboard engagement with shipping operators were common success factors across all case studies. Furthermore, strategies to promote corporate social responsibility also show promise for increasing compliance by vessels. These findings can be used to substantiate and inform future management plans for lowering ship strike risk in areas across the world in need of evidence-based support of voluntary speed-restriction 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".