Bibliographic record
Abstract
The 6th Asia-Pacific Conference on Disaster Medicine S3gration of the World Trade Center towers represented the largest structural collapses in history.Only weeks later, the eastern United States found itself gripped by a series of anthrax letter attacks, which ultimately caused inhalational anthrax in 11 (killing 5), produced cutaneous anthrax in 11 others, and led to tens of thousands of others potentially exposed to anthrax-tainted mail being placed on a 60-day course of prophylactic antibiotics.Are these events random and idiosyncratic, or do they provide a sobering window into what the next 99 years of the 21st Century portend?This presentation examined the types of disasters likely to occur during the 21st Century, and examined the forces likely to be responsible.From global warming to geopolitical tribalism, the most important factor is an ever-expanding human population trapped within a finite planet, pitting growing demands against limited resources.Medical disasters are and will continue to be a frequent result of this disequilibrium.Regardless of the root causes of future disasters, disaster managers will be faced with planning and preparing for events that impact their communities in ways that both are routine and unprecedented.If there is any single lesson to be learned from recent catastrophic medical disasters in the world, it is that adequate medical disaster response depends on local resources in the initial period after an event.For this reason, it is imperative that those involved in Disaster Medicine become actively involved in the development of local emergency medical resources, both out-of-hospital and in-hospital, in areas of the world in which emergency medicine is underdeveloped.
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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".