Feature Analysis and Comparison of Prediction Methods for Fire Accidents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fire is one of the most common production safety accident. The trend of fire can be mastered by analyzing the historical data. This paper explores the features of recent fires in China, predicts fire by two methods, namely, grey theory, and grey Markov theory, and compares the prediction results of the two methods. The results show that: the number of fires in China increased greatly in 2013; Since 2014, the number of fires, as well as the number of deaths, the number of injured, and property loss induced by fires were declining. The maximum relative error of grey prediction was 5.8%, and that of grey Markov prediction was 5%; grey theory is less accurate in fire prediction than grey Markov prediction. According to the causes and features of fires, several preventive measures were put forward. The research results provide insights into the prevention of fires and protection of production safety.
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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 it