Managing Biodiversity in the Port Sector: Experiences from Three World Ports
Bibliographic record
Abstract
Abstract The biodiversity crisis, highlighted for example by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), indicates that up to one million species face extinction risks. Biodiversity’s significance is paramount for ecosystem functionality, offering vital services like clean air, water, food, and medicine. Port and coastal areas, often of regional or national importance, benefit from biodiversity. Ecosystems such as wetlands and dune fields around ports maintain water quality, filter pollutants, offer habitats for various species, and act as buffers against industrial pollution. However, port activities can negatively affect regional flora and fauna, disrupting ecological equilibrium. Some port authorities, like Rotterdam, have initiated measures to monitor and conserve regional biodiversity. The increasing responsibility of port authorities to preserve biodiversity necessitates further research to comprehend ports’ impact on biodiversity and devise sustainable management strategies. This paper examines the sufficiency of current port governance models for biodiversity conservation, proposing a framework based on the experiences of Vancouver, Brisbane, and Rotterdam ports. The analysis reveals that biodiversity loss poses significant challenges for ports. Despite best practices, biodiversity conservation faces hurdles like target fragmentation, interaction with climate change policies, financial constraints, expanding port authority responsibilities without appropriate governance tools, and intricate local stakeholder cooperation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".