Supporting Risk Assessment: Accounting for Indirect Risk to Ecosystem Components
Bibliographic record
Abstract
The multi-scalar complexity of social-ecological systems makes it challenging to quantify impacts from human activities on ecosystems, inspiring risk-based approaches to assessments of potential effects of human activities on valued ecosystem components. Risk assessments do not commonly include the risk from indirect effects as mediated via habitat and prey. In this case study from British Columbia, Canada, we illustrate how such "indirect risks" can be incorporated into risk assessments for seventeen ecosystem components. We ask whether (i) the addition of indirect risk changes the at-risk ranking of the seventeen ecosystem components and if (ii) risk scores correlate with trophic prey and habitat linkages in the food web. Even with conservative assumptions about the transfer of impacts or risks from prey species and habitats, the addition of indirect risks in the cumulative risk score changes the ranking of priorities for management. In particular, resident orca, Steller sea lion, and Pacific herring all increase in relative risk, more closely aligning these species with their "at-risk status" designations. Risk assessments are not a replacement for impact assessments, but-by considering the potential for indirect risks as we demonstrate here-they offer a crucial complementary perspective for the management of ecosystems and the organisms within.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".