Bibliographic record
Abstract
We begin the issue with a letter by Kevin Konty et al. about a paper by Walter Piegorsch et al. (1) which used a place-based vulnerabilty index to characterize 132 U.S. cities.Konty and colleagues from the New York City Department of Health and Mental Hygiene question the data and conclusions.The authors respond that their focus is on event consequences, not on the ability to predict future terrorist events.Five of the ten papers in this issue are contributions to risk assessment.John Bukowksi's perspective paper examines cancer risk among workers exposed to chloroprene.With a grant from the International Institute of Synthetic Rubber, he focuses on liver, lung, and lymphohematopoietic cancers, which early studies found to have relatively high numbers of cases.Using a set of studies from across the world, Bukowksi observes a strong healthy worker effect, which could have obscured small excess risks.Small increased risks were suggested by internal or company-specific analyses, but, he asserts, these may be explained by confounding factors.Hamid Mohtadi and Antu Panini Murshid, supported by the U.S. Department of Homeland Security, assess historical trends in food sector bioterrorism.They point to a disturbing shortening in the re-occurrence period for attacks on food supplies.Specifically, they find that the frequency of catastrophic terrorist events, i.e., those with large numbers of casualties, is on the rise and the average reoccurrence period for such scale attacks is on the decline.Thus, for example, by the year 2025, an attack leading to 5,000 casualties would be expected to occur every 20 months, if not more often.Funded by the United States Department of Agriculture, Frank Koch et al. examine factors that influence the spread of pests across the landscape, in this case Sirex noctilio, a non-native wood wasp recently detected in the United States and Canada.They concentrate on how increased uncertainty in a risk model's numeric assumptions affects the spread of pests.
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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.236 | 0.182 |
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".