Characterizing the social-ecological importance of coastal marine locations: integrative challenges, insights and solutions from the Pacific north coast of British Columbia
Bibliographic record
Abstract
Human exploitation of earth's ecosystems has impacted the flow of ecological services, many with complex links to human health and well-being. The need to understand and plan for these impacts in an integrative manner is today an imperative. Yet, their integration into the planning process has been largely unsuccessful. In Canada, the Canadian Environmental Assessment Act (CEAA) was established to achieve this integration. Yet, despite decades of effort there has been limited progress in practice. Thus, the aim of this research was to contribute new knowledge and insights to the challenge of integrating a broad range of social and ecological concerns into the environmental planning and management process, focussing on pragmatic solutions. A scoping review of the literature revealed key underlying issues affecting integration. These were discussed and contextualized to the CEAA mandated Environmental Assessment (EA) process, and a number of recommendations made for improved integration. The integration challenge was then examined within a spatial context. Two approaches to integrated spatial analyses were investigated. The first approach focussed on available marine spatial social, ecological, economic and protection legislation data analyzing the data both singly to detect statistically significant clustering of high value or high incidence data (hotspots) and collectively to detect areas of agreement (overlaps). The analyses provided a perspective on the spatial distribution of marine social-ecological-economic hotspots. The integration was, however, challenged by the characteristics of the underlying data including differing approaches to data collection and units of measure. The second approach to integrated spatial analysis was based on expert spatial knowledge of the social-ecological system, and was termed expert informed geographic information systems (xGIS). Important social-ecological spaces were similarly detected using xGIS. It was found that xGIS allowed for a broader range of values to be co
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".