A voluntary conservation agreement reduces the risks of lethal collisions between ships and whales in the St. Lawrence Estuary (Québec, Canada): From co-construction to monitoring compliance and assessing effectiveness
Bibliographic record
Abstract
Lethal collisions with ships are limiting the recovery of several at-risk whale species worldwide. In the St. Lawrence Estuary (Quebec, Canada), the endangered blue whale and of special concern fin whale are among the migratory species subject to collisions with large ships. In 2011, a working group composed of representatives from the maritime industry, the government, non-governmental organizations, and academia was created to explore solutions to mitigate ship-whale collisions in the St. Lawrence Estuary. Adopting an adaptive risk management framework, the working group took advantage of the best available scientific data and tools to co-construct realistic collision mitigation options and evaluate their likely benefits for whale conservation and costs for the industry. In 2013, the working group recommended the implementation of voluntary measures to mitigate collision risks, consisting of a slow-down area, a no-go area, and a caution area; a recommended route was added in 2014. Along with the voluntary framework, the working group agreed to continuously monitor compliance with and assess effectiveness of these mitigation measures. After the fourth year of implementation, voluntary measures showed encouraging results, with a reduction of up to 40% of lethal collision risks with fin whales in the highest density area. This reduction in risk is mainly related to ship speed reduction in the slow-down area from 14.1 ± 2.6 knots in 2012 to 11.3 ± 1.7 knots since 2014. The presence of a mandatory pilotage area overlapping with the slow-down area was instrumental to facilitate communication about the mitigation measures, with the pilotage corporation sitting as a regular member of the working group. This resulted in significantly slower speeds in the slow-down area for ships with a pilot from the pilotage corporation onboard compared to those without (-0.8 knots, p-value < 0.001). It is also likely to explain the weaker compliance of the maritime industry with the no-go area located outside of the mandatory pilotage area. Other factors of success include: the continuous dedication of the government to a voluntary and transparent participatory process; the use of available data, tools and institutions; the presence of an environmental certification program representative in the working group; and the adoption by consensus of an adaptive risk management approach. The traditional regulatory approach to conservation is often blamed for its focus on deterring negative behaviors, doing nothing to encourage and reward positive ones. In agreement with other case studies, the benefits of the voluntary measures implemented in the St. Lawrence Estuary include the pro-active commitment from the industry (which is likely to reduce conflicts with regulators), the greater flexibility and freedom that allowed to come up with cost-effective and tailored-made mitigation measures, and the fast achievement of conservation gains. More importantly perhaps, the human and working capital built throughout the concertation process have the potential to be a fundamental cornerstone in dealing with more complex issues such as the chronically increasing level of underwater noise in whale habitats.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".