Back to the future: using landscape ecology to understand changing patterns of land use in Canada, and its effects on the sustainability of coastal ecosystems
Bibliographic record
Abstract
Abstract Mercer Clarke, C. S. L., Roff, J. C., and Bard, S. M. 2008. Back to the future: using landscape ecology to understand changing patterns of land use in Canada, and its effects on the sustainability of coastal ecosystems. – ICES Journal of Marine Science, 65: 1534–1539. In Canada, concerns are mounting that the coastal environments may be more affected by human activities than is evidenced by current monitoring and assessment of environmental quality. Holistically orientated approaches to coastal management have concluded that indicators of coastal sustainability must include a wider array of factors that go beyond marine ecosystem health to include the health and well-being of coastal terrestrial environments and human communities. Research is needed to bridge the disciplinary and jurisdictional barriers that hamper better understanding of the relationships between terrestrial and marine ecosystems, and to help recognize the role of humans as both a contributing and an affected species in the coastal ecotone. Our examination of past and current knowledge of conditions along the Atlantic shore of Nova Scotia led us to challenge the predominant view that all is well along Canadian coasts. Using an interdisciplinary approach derived from landscape ecology, we examined international, national, and local efforts to assess management indicators against factors that gauge their relevance to marine- and land-development planning and management. We propose a new context for indicators, one that challenges scientists to provide decision-makers with information that can be used to drive social change, avoiding or mitigating human activities and sustaining coastal ecosystems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".