Marine diseases as a threat to society: Adopting and advancing the UNDRR risk framework
Bibliographic record
Abstract
Marine diseases change ecosystem dynamics and functioning, and modify ecosystem service (e.g. food) provisioning. Understanding marine diseases’ occurrence and frequency, and consequences and impacts thereof, is crucial for humans and nature alike, though the implications for society beyond human health have received little attention in scientific debates yet. This study advocates for the uptake of marine diseases into hazard landscapes currently being evaluated and discusses the different components of risks that marine diseases pose to societies: Adopting the analytical lens of the UNDRR risk framework to oyster farms as a specific case, we explore disease outbreaks in those as hazards to society. Looking at associated exposure and vulnerability, potential risk reduction options are elaborated. The framework is broadened by including indirect and spill-over effects within the social-ecological system – to local coastal communities. Marine diseases management is challenged by the fluidity of the ocean and fragmented governance structures. To reduce social-ecological repercussions and overall risks for society of disease outbreaks we thus endorse for a thorough risk evaluation and sensible, anticipatory communication. • We apply the UNDRR risk framework, adopting it to the case of marine diseases. • Oyster diseases as a hazard to oyster populations and coastal societies is analysed, to then develop and discuss risk reduction options. • Risks and social-ecological impacts of marine disease outbreaks can be reduced by risk evaluation and proactive communication.
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| 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".