Bibliographic record
Abstract
We begin with a Letter to the Editor from Ted Yellman, who refers to recent articles about the definition of frequency by Tony Cox. He explains why he disagrees with Cox about the impossibility of defining frequency. Cox responds with the assertion that no definition can agree with both the ordinary meaning of frequency, and also apply to both nonexponential and exponential time distributions between events. B. John Garrick also writes a short response, noting that it is sometimes best to define terms in ways that allow the solving of a specific problem. The first article in the issue is an interesting perspective by Igor Linkov et al. that compares government use of weight of evidence (WOE) in decision-making with neuroscience research that shows that the human brain uses WOE processes to assess and value evidence. The authors consider how these parallels might be directed at improving organizational decision-making. Four papers are about food and other biological risks. Natalie Commeau et al. propose and test a Bayesian model to determine within-batch and between-batch food contamination. The authors test their approach with Listeria monocytogenes in pork breast used to produce diced bacon and cold salmon. Supported by the United Arab Emirates, Christopher Davidson et al. examine population exposure to methylmercury (in seafood) and pesticides (in fruits and vegetables). Findings show that exposure to pesticides is not observed to be a major concern, while exposure to methylmercury in seafood is. We often read about exposure of rural populations who consume locally caught fish. Based on a survey of fishermen in a rural creek in northern Alabama Ellen Ebert et al. conclude that there is little fish consumption, and hence not much can be learned about exposure. Funded by the US Environmental Protection Agency and US Department of Homeland Security, Toru Watanabe et al. create dose-response assessments for wild-type influenza A virus reassortants (mixes of genetic materials). The authors find dose-response is most effectively characterized by a Beta-Poisson model with virus subtype as a strong predictor. Technological risks are the subject of a second cluster of papers in this issue. Gary Gaukler et al. challenge the capacity of current technology used in U.S. ports to detect smuggling of nuclear materials inside containers. They find the current system can miss nuclear materials, and they propose and test a more complex approach. Suyi Li et al. examine 87 studies of collisions and groundings in maritime waterways, pointing to human errors as critical causal factors. Barbara Miller and Janas Sinclair examine risk perceptions in coal mining communities in order to try to understand the most salient issues for residents. Using focus groups, the authors find that the perception or presence of stigma, economic dependence, and danger pulled respondents in different directions. Carmen Keller and colleagues study public perception of new generation nuclear power plants among German and French-speaking Swiss. Opponents of new nuclear power plants tend to associate the facilities with far more images than proponents described, such as risk, negative feelings, accidents, radioactivity, waste disposal, military use, and negative consequences for health and environment. Michael Greenberg shares a commentary on the article, followed by a response from the authors. The final articles reintroduce several long-standing risk-related issues. Hwashin Hyun Shin et al. model the impact of NO2 concentrations on disease outcomes in Canadian cities. They find no evidence for time trends in outcomes but do find increasing differences among Canada's regions. Focusing on Chinese cities where people and hazards are increasingly proximate, Yafei Zhou and Mao Liu draw risk distribution contours. Using a Chinese city to illustrate their approach, the authors assert their approach as an urban planning tool. Children's exposure from ingestion of soil is a much-debated topic. Edward Stanek III et al. present two articles in this issue based on data obtained from four studies of aluminum (Al) and silicon (Si) ingestion in the northern United States. Funded by the Dow Chemical Company, the authors conclude that much of the variability reported in mass-balance soil ingestion studies of children is attributable to study design variations and to variation in trace element intake from food, and in soil. The authors argue that about 12% of subjects have to be excluded because their data are unsuitable for inclusion in studies intended to be extrapolated to the population of children.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".