Demographic yearbooks as a source of weather-related fatalities: The Czech Republic, 1919–2022
Bibliographic record
Abstract
Abstract. Demographic yearbooks of the Czech Republic, prepared by the Czech Statistical Office for the period 1919–2022, contain official figures for the number, gender, and age of fatalities attributed to excessive natural cold, excessive natural heat, lightning, natural disasters, air pressure changes, and falls on ice or snow, covering a 104-year period or its parts. These yearbooks, influenced by evolving international classifications of diseases, tend to underestimate the fatality numbers for excessive natural heat, natural disasters, and especially air pressure changes. Out of a total of 9,259 weather-related fatalities (with a mean annual rate of 89.0 fatalities), 74.9 % were caused by excessive natural cold and 19.3 % by lightning. Except for a zero linear trend in natural disasters, statistically significant decreasing trends were found for lightning fatalities, and increasing trends for excessive natural cold, excessive natural heat, and falls on ice or snow. Males and seniors aged ≥65 years were the most common gender and age categories affected. The number of fatalities attributed to excessive natural cold has partly increased as a result of the gradually aging population and the rise in the number of homeless people since the 1990s. A statistically significant relationship between cold-related fatalities and mean January–February and winter (December–February) temperatures was established, evidenced by high negative correlation coefficients. Lightning deaths have notably decreased since the 1970s, primarily due to a significant reduction in the number of people employed in agriculture, an increase in urban population, better weather forecasting, lifestyle changes, and improved medical care. Although there is a significant positive correlation between these fatalities and the number of days with thunderstorms, the relationship is relatively weak. The results obtained for the Czech Republic align well with similar studies in Europe and elsewhere. While the demographic yearbooks cover only a part of weather-related fatalities, their circumstances, and characteristics, combining them with other similar databases is crucial to gain necessary knowledge usable in risk management for the preservation of human lives.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".