Cumulative effects of planned industrial development and climate change on marine ecosystems
Bibliographic record
Abstract
With increasing human population, large scale climate changes, and the interaction of multiple stressors, understanding cumulative effects on marine ecosystems is increasingly important. Two major drivers of change in coastal and marine ecosystems are industrial developments with acute impacts on local ecosystems, and global climate change stressors with widespread impacts. We conducted a cumulative effects mapping analysis of the marine waters of British Columbia, Canada, under different scenarios: climate change and planned developments. At the coast-wide scale, climate change drove the largest change in cumulative effects with both widespread impacts and high vulnerability scores. Where the impacts of planned developments occur, planned industrial and pipeline activities had high cumulative effects, but the footprint of these effects was comparatively localized. Nearshore habitats were at greatest risk from planned industrial and pipeline activities; in particular, the impacts of planned pipelines on rocky intertidal habitats were predicted to cause the highest change in cumulative effects. This method of incorporating planned industrial development in cumulative effects mapping allows explicit comparison of different scenarios with the potential to be used in environmental impact assessments at various scales. Its use allows resource managers to consider cumulative effect hotspots when making decisions regarding industrial developments and avoid unacceptable cumulative effects. Management needs to consider both global and local stressors in managing marine ecosystems for the protection of biodiversity and the provisioning of ecosystem services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".