Ship-strike forecast and mitigation for whales in Gitga’at First Nation territory
Bibliographic record
Abstract
As marine traffic increases globally, ship strikes have emerged as a primary threat to many baleen whale populations. Here we predict ship-strike rates for fin whales Balaenoptera physalus and humpback whales Megaptera novaeangliae in the central territorial waters of the Gitga’at First Nation (British Columbia, Canada), which face increases in existing marine traffic as well as new liquified natural gas (LNG) shipping in the next decade. To do so, we utilized Automatic Identification System (AIS) databases, line-transect surveys, shore-based monitoring, whale-borne tags, aerial drone-based focal follows, and iterative simulations. We predict that by 2030, whale encounters will triple for most vessel types, but the change is most extreme for large ships (length >180 m) in prime whale habitat, in which co-occurrences will increase 30-fold. Ship-strike mortalities are projected to increase in the next decade by 2.3× for fin whales and 3.9× for humpback whales, to 2 and 18 deaths yr -1 , respectively. These unsustainable losses will likely deplete both species in the coastal region of BC. Models indicate that the largest single source of mortality risk in 2030 will be from the LNG Canada project. Of the mitigation options we evaluated, a 10 knot speed ceiling for all large ships is potentially effective, but the best measure for guaranteed mitigation would be seasonal restrictions on LNG traffic. While certain data gaps remain, particularly with respect to humpback whales, our predictions indicate that shipping trends within Gitga’at waters will impact whale populations at regional levels. We provide our analysis in the R package ‘shipstrike’.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".