Bibliographic record
Abstract
Abstract There is a major difference in population-weighted lightning fatality rates between the lower fatality rates in developed countries and the higher fatality rates in developing countries. The large decrease in annual rates of population-weighted lightning fatalities in the United States is described over the last century. A similar large reduction in lightning fatality rates has occurred during recent years in Australia, Canada, Japan, and western Europe, where there has also been a change from a mainly rural agricultural society to a primarily urban society. An important accompanying aspect of the lower casualty rates has been the widespread availability of lightning-safe large buildings and fully enclosed metal-topped vehicles, as well as much greater awareness of the lightning threat, better medical treatment, and availability of real-time lightning information. However, lightning exposure for many people in lesser-developed countries is similar to that of a century ago in developed countries. The number of people living in these areas may be increasing in number, so the number of people killed by lightning may be increasing globally due to these socioeconomic factors. It can be difficult to locate national lightning fatality data because of their mainly obscure publication sources. The present paper synthesizes lightning fatality data from 23 published national-scale studies during periods ending in 1979 and later, and maps these fatality rates per million by continent.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".