The Great East Japan Earthquake, Tsunamis, and Fukushima Daiichi Nuclear Power Plant Disaster: Lessons for Evidence Integration from a WADEM 2017 Presentation and Panel Discussion
Bibliographic record
Abstract
In April 2017, some of the health impacts of the 2011 Great East Japan Earthquake, tsunamis, and resultant Fukushima Daiichi nuclear power plant disaster (Okuma, Fukushima Prefecture, Japan) were presented at the 19th Congress of the World Association for Disaster and Emergency Medicine (WADEM; Madison, Wisconsin USA) in Toronto, Canada. A panel discussion was then opened by asking audience members about their experiences in their own countries, and how they would suggest taking steps to reach the goals of the Sendai Framework for Disaster Risk Reduction 2015-2030. This paper summarizes the presentation and panel discussion, with a particular focus on the common problems identified in understanding and reducing health risks from disasters in multiple countries, such as the ethical and practical difficulties in decision making on evacuating vulnerable populations that arose similarly during the Fukushima nuclear disaster in 2011 and Hurricane Ike's approach to Galveston (Texas USA) in 2008. This paper also highlights the need for greater integration of research, for example through increased review and collation of evidence from different disaster settings to identify common problems and possible solutions, which was identified in this panel session as a precursor to fulfilling the goals of the Sendai Framework.Leppold C, Ochi S, Nomura S, Murray V. The Great East Japan Earthquake, tsunamis, and Fukushima Daiichi nuclear power plant disaster: lessons for evidence integration from a WADEM 2017 presentation and panel discussion. Prehosp Disaster Med. 2018;33(4):424-427.
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 | 0.384 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".