A Study on Fire Data Analysis in Korea, Japan and USA(3) Deaths and Injuries Due to Fires
Bibliographic record
Abstract
The following matters were confirmed through the analysis of casualties due to fires in Korea, Japan, and the U.S. in this paper. 1 Korean statistics are not the most detailed of the three countries about casualties due to fires, so we need to have detailed statistics of them on casualties more. 2. Korean deaths are the lowest by 10-11 people due to fires per one million of population. Those of Japan are 15-17 and about 12 people in the U.S.; decreased about 2/3 only for a quarter of a century. 3. Korean deaths are on the decrease about 1.5 people per 100 cases due to fires,3.5 in Japan and 0.2-0.3 in the U.S. Likewise, Korean injuries are on the decrease per 100 cases due to fires From 14.9 in 1977 to 5.1 in 2001 and 5.3 in 2002. In the U.S., the figure was 1.0-1.6. but after the year 1994, it was 1.2 or so. It tends to some increase to 2.6-2.8 in Japan. Therefore, when fires are happened, the death probability is the highest in Japan and 15 times higher than that of the U.S. The injury probability is the highest in Korea and 5 times higher than that of the U.S. 4. Fire deaths rate is the highest in the U.S. about due to home fires (including apartments) among all deaths. Japan tends to decrease of . Recently, in case of Korea. it is similar level to that of Japan. 5. Korean aged people of 65 years old and over exceeded by in 2000 and entered an aging society, so It Is time to Investigate and take effect policies to reduce the death of the aged . Japan has ahead a super-aged society that exceeds of the people over the age 65, and many of them die of fire. Consequently, Japan has taken effect policies to reduce deaths from 10 years or more than before. Therefore, it is a good proposal to analyze the policies of Japan deeply and study introduction of them.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".