Safety index of heat wave mortality using big data
Bibliographic record
Abstract
Safety-related research using big data is still in its early stage in Korea.Recently, we have tried to solve some safety-related problems using big data.In this research, we will attempt to solve heat wave-related death which is a significant potential concern associated with climate change.Although deaths from heat disorders are a direct effect of heat wave incidences, only a few studies have addressed the causal factors between heat wave incidences and deaths from heat disorder.Regression analysis is applied to deduce the causal factors that affect the number of deaths from heat disorders (NDHD) in South Korea by using time-series dataset, which are the NDHD and climate data.Both are observational data from 1994 to 2012, collected from the National Statistical Office and the Korean Meteorological Agency, respectively.As a result, the duration of a heat wave and the age of the population are highly correlated with the NDHD.Based on this correlation, we also analyze the safety index of heat wave mortality.The paper is structured as follows: First of all, it presents the data and methods, including our strategy for analysis of heat wave incidences based on observational and climate modeling datasets.The next section presents the results of regression models applied for predicting heat wave deaths in Korea and discusses the statistical analysis of the results.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.002 | 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".