Natural Disasters, Corpses and the Risk of Infectious Diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent occurrence of the category 4 Hurricane Katrina devastated the United States? Gulf Coast. The hurricane caused widespread destruction and flooding, and left hundreds of thousands of people homeless. The mounting death toll was reported at almost 300 deaths as of September 8, 2005 (1,2). The unfolding events and high death toll have left an unusual situation in which there are many decomposing corpses either lying on the streets or floating in the flood waters. The presence of these corpses in open settings, such as in public places and in the water that has inundated much of the city of New Orleans, naturally raises concerns about the occurrence of infectious disease epidemics (3). In the aftermath of large natural disasters, instinctive uncertainties arise among workers and the general population with respect to the appropriate handling and disposal of dead bodies and human remains. Given the recent occurrence of Hurricane Katrina as a large natural disaster and the unprecedented setting of the numerous corpses requiring disposal, it was considered timely to review the infectious disease risks associated with the handling of dead bodies.
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.
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 | 0.001 |
| 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) | 0.000 | 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 it