Expert elicitation spreadsheet from Accounting for direct and indirect cumulative effects of anthropogenic pressures on salmon- and herring-linked land and ocean ecosystems
Bibliographic record
Abstract
Salmon and herring support both land and ocean predators and are critical to ecosystem resilience. Their linkages across land and sea realms make them highly susceptible to human activities, which can have flow-on effects up the food web. We quantify and compare the potential cumulative effects of human-driven pressures on interdependent species in salmon- and herring-linked ecosystems of western Canada using a risk assessment methodology. Adding indirect risks resulted in 68% greater total risks for land species than for direct risk alone, versus 15% for marine species. Inclusion of climate change pressures resulted in the greatest change in risk for low trophic marine species and habitats (greater than 25% increase). Forestry-related pressures accounted for the highest risk to all species and projected management of these pressures resulted in a total reduction of risk across all ecosystem components that was greater than 14% greater than management of fisheries pressures. Ignoring land food web linkages and pressures resulted in missing greater than 40% of the total cumulative risk for salmon and herring. This simple framework can be used to evaluate potential risk of existing human uses and future change to inform immediate management of linked land-sea ecosystems and help species avoid the ‘death by a thousand cuts'.This article is part of the theme issue ‘Nurturing resilient marine ecosystems’ compiled and edited by Daniela Schmidt and Tayanah O'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.055 |
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".