Holistic environmental monitoring in ports as an opportunity to advance sustainable development, marine science, and social inclusiveness
Bibliographic record
Abstract
Ports play a central role in our society, but they entail potential environmental risks and stressors that may cause detrimental impacts to both neighboring natural ecosystems and human health. Port managers face multiple challenges to mitigate risks and avoid ecosystem impacts and should recognize that ports are embedded in the wider regional coastal ecosystem. Cumulative impacts of anthropogenic stressors have the potential to further burden the existing suite of natural stressors, particularly where ports are located in embayments and estuaries. Environmental monitoring in ports should thus develop a comprehensive, holistic, multilayered approach integrated in the wider ecosystem that will help managers better achieve sustainable development, a major goal of the United Nations’ 2030 agenda and Decade of Ocean Science for Sustainable Development (2021–2030). This practice bridge showcases the experience of the second Canadian Healthy Ocean Network (CHONe2) in Baie des Sept Îles (BSI, Quebec; the fourth largest industrial port in Canada) laying the foundations of holistic environmental monitoring in ports. We describe the partnership model (i.e., engaging scientists, local authorities, an independent organization, and local industries), synthesize the multidisciplinary studies that turned environmental monitoring into a systemic investigation of the biological and physical components of BSI, integrate the developed scientific knowledge into a social–ecological–environmental system, present an innovative near real-time monitoring approach, and discuss implications for management and policy. The CHONe2 experience in BSI aligns with the decade’s road map for sustainable development and provides elements that could be adapted to other commercial ports. By suggesting a set of best practices (e.g., multidisciplinarity, transparency, inclusivity, participatory modeling), we hope to spark new interest in environmental monitoring as a path to conciliate development and sustainability of ports and other high-use marine areas.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".