Cumulative impact mapping and vulnerability of Canadian marine ecosystems to Anthropogenic activities and stressors
Bibliographic record
Abstract
The consideration of cumulative effects, in efforts ranging from environmental assessment to marine spatial planning, continues to pose challenges for both scientists and managers. The assessment of cumulative effects is a rapidly evolving field with a diversity of approaches and methodologies. Cumulative impact mapping is one established method for representing the spatial impacts of multiple activities and stressors. Since its first publication by Ben Halpern and colleagues in 2008, cumulative impact mapping has been applied at various spatial scales in regions around the world, including Canada. It is an established, semi-quantitative model that spatially represents the additive effects of human activities and stressors on marine ecosystems. The cumulative impact mapping model involves compilation and standardization of high-quality spatially explicit marine data. Three sets of data are required: 1. Spatial representation of human activities and/or stressors, 2. Spatial representation of habitats (or species), and 3. A matrix of scores to represent the relative vulnerability of each habitat to each activity or stressor. Impact scores are summed across all habitats and activities for each grid cell to produce a map of relative cumulative impact. The results of the model allow visualisation of the relative cumulative impact within the target region, highlighting areas most and least affected by human activities. In this paper, we give an overview of the cumulative impact mapping method and its application in Canadian waters. We present the results of an expert review of the vulnerability matrices for Pacific and Atlantic Canada and the suggested changes for use in cumulative impact mapping efforts going forward. Finally, we discuss the limitations and assumptions of the method and its applicability for various management contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".