Assessing and managing multiple risks in a changing world—The Roskilde recommendations
Bibliographic record
Abstract
Roskilde University (Denmark) hosted a November 2015 workshop, Environmental Risk-Assessing and Managing Multiple Risks in a Changing World. This Focus article presents the consensus recommendations of 30 attendees from 9 countries regarding implementation of a common currency (ecosystem services) for holistic environmental risk assessment and management; improvements to risk assessment and management in a complex, human-modified, and changing world; appropriate development of protection goals in a 2-stage process; dealing with societal issues; risk-management information needs; conducting risk assessment of risk management; and development of adaptive and flexible regulatory systems. The authors encourage both cross-disciplinary and interdisciplinary approaches to address their 10 recommendations: 1) adopt ecosystem services as a common currency for risk assessment and management; 2) consider cumulative stressors (chemical and nonchemical) and determine which dominate to best manage and restore ecosystem services; 3) fully integrate risk managers and communities of interest into the risk-assessment process; 4) fully integrate risk assessors and communities of interest into the risk-management process; 5) consider socioeconomics and increased transparency in both risk assessment and risk management; 6) recognize the ethical rights of humans and ecosystems to an adequate level of protection; 7) determine relevant reference conditions and the proper ecological context for assessments in human-modified systems; 8) assess risks and benefits to humans and the ecosystem and consider unintended consequences of management actions; 9) avoid excessive conservatism or possible underprotection resulting from sole reliance on binary, numerical benchmarks; and 10) develop adaptive risk-management and regulatory goals based on ranges of uncertainty. Environ Toxicol Chem 2017;36:7-16. © 2016 SETAC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".