Rethinking disaster risk for ecological risk assessment
Bibliographic record
Abstract
While disaster events are consequential, they are rare. Ecological risk assessment processes tend to estimate risk through an “expected value” lens that focuses on the most probable events, which can drastically underappreciate the importance of rare events. Here, we show that expected value and average risk-based calculations underappreciate disaster events through questionable assumptions about equally weighing high probability low impact events with low probability high impact events, and in modeling probability as a chance among an ensemble of possible futures when many contexts of ecological risk assessment are focused on a single entity over time. We propose an update to ecological risk assessment that is specifically inclusive of disaster risk potential by adopting analytical processes that estimate the maximum hazard or impact that might be experienced in the future, borrowing from the practice of modeling “Value at Risk” in financial risk contexts. We show how this approach can be adopted in a variety of data contexts, including situations where no quantitative data is available and risk assessment is based on expert judgement, which is common for ecological risk assessment. Increased exposure to environmental variation requires assessment tools to better prepare for, mitigate, and respond to disasters.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".