Machine learning reveals drivers of cold-related illness during energy infrastructure attacks in Wartime Ukraine
Bibliographic record
Abstract
During the Russian invasion of Ukraine, targeted attacks on energy infrastructure exposed civilians to heightened cold-related health risks. This study aimed to: (1) characterize respiratory infections and cold-related injuries during the conflict; (2) identify vulnerable sociodemographic groups; (3) assess household adaptations; and (4) evaluate how winter preparation influenced health outcomes. We surveyed 2311 households across 24 Ukrainian oblasts during the winter of 2022–2023. One adult per household provided data on demographics, winter preparations, housing, heating, and access to services. Machine learning models were used to predict respiratory infections, symptoms, and cold injuries, based on sociodemographic and household factors. Respiratory infections affected 75.2% of participants, and 3.76% reported cold injuries, rising to 10% among older adults. Larger households experienced more respiratory infections, while areas under Russian control reported higher rates of cold injury. Key predictors of respiratory infections included age, household size, financial stability, and heating practices; cold injuries were predicted by age, region, anxiety, and household size. This is the first study to apply machine learning in examining the health impacts of cold-related events following energy infrastructure attacks in an active conflict zone. Our findings underscore the vulnerability of older adults and the widespread burden of respiratory infections, highlighting the need for targeted cold injury prevention in conflict-affected and cold-climate regions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".