Fatalities Associated With the Termination of Electrical Services
Bibliographic record
Abstract
The worldwide dependence on electricity to support the needs of today's society is often taken for granted and, as such, the hazards associated with the termination of electrical services are often neglected. Whether electrical services are unintentionally terminated as the result of severe weather or intentionally terminated as the result of nonpayment of utility bills, the ensuing conditions may lead to injury or death in affected individuals. We performed a retrospective review of all deaths investigated by the Onondaga County Medical Examiner's Office between 1999 and 2004. Our case database was searched for causes of death that included hypothermia, hyperthermia, carbon monoxide, fire, electrocution, and/or electricity. Further review of these cases was undertaken to determine the potential relationship between the death and the termination of electrical services. Seven fatalities were found to be associated with the termination of electrical services. Four fatalities resulted from its unintentional termination and 3 were a consequence of intentional termination. In reporting these deaths, we hope to emphasize the potential dangers associated with the termination of electrical services and explore the informational programs and public health laws that are in place to limit the associated potential negative outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".