Comparative analysis of the relationship between leukemia trends with forest fires and solar activity in different age groups
Bibliographic record
Abstract
BACKGROUND: Studying the etiology of leukemia is necessary to develop measures to prevent this pathology. AIM: Conduct a comparative analysis of the relationship between trends in the incidence of leukemia with forest fires and solar activity in different age groups. MATERIAL AND METHODS: Information on the incidence of leukemia in Russia in 1990–2019 was provided by the Moscow Research Oncological Institute named after P.A. Hertsen. Data on solar activity (Wolf numbers) and the number of forest fires were taken from open sources. A Pearson correlation analysis of dynamic series of environmental factors and the incidence of leukemia was carried out in 11 iterations with a time delay (lag) of 0–10 years. The obtained data was compared with similar information for the regions of Russia, the USA and Canada. RESULTS: A trend towards an increase in the incidence of leukemia in children and adults has been established in the populations of Russia, the USA and Canada. A relationship between the number of forest fires and the incidence of leukemia was found in 35 regions of Russia; the average correlation coefficient and lag were comparable to those previously identified in the Khabarovsk Territory. The correlation of Wolf numbers with the frequency of leukemia has been established in pediatric and full-age populations of Russia, Canada and the USA. In the full-age population of Russia, a tendency towards an increased connection between the frequency of leukemia and solar activity was revealed: in 1990–1999, the correlation was 0.697; in 2000–2009 it increased to 0.815; in 2010–2019 reached a very strong level (0.920), while the lag decreased from 6 to 4 years. CONCLUSION: Fluctuations in the incidence of leukemia in all age groups in Russia correlate with the number of forest fires and solar activity.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".