Mapping Analysis of Active Fire Protection System on Dormitory Building in X University
Bibliographic record
Abstract
Introduction: Fire cases increase in residential buildings because short circuits, at dormitories are mostly affected by human factors. X university dormitory building is a place for 24 hours of students’ activities with a lot of electricity consumption, bad behavior; overpowering extensions, and many flammable objects such as paper and furniture. Research objectives are to investigate the appropriateness and mapping needs of fire protection systems with regulations. Methods: The research is a semi quantitative analysis. Primary data is conducted with observations by using a checklist form that refers to the regulations and results of managers' interviews. The obtained data are categorized into good with suitability >79-100%, moderate ≥60%-79% and poor <60%. Result: The appropriateness of the active fire protection system is under 60% or in the poor category. The appropriateness of the fire extinguisher is 47.77%, the hydrant is 58.75%, the alarm is 18.75% and the heat detector is 35.90%. There is no sprinkler installation in the dormitory building and no commitment from the top management regarding occupational safety and health. Fire extinguisher’s need is 10 tubes per floor, the number of hydrants have been fulfilled, alarm requires an additional 1 unit on each floor and a heat detector requires a total of 114 units. Conclusion: The active fire protection system is still under 60% (poor category) and no maintenances. Based on the aforementioned mapping results, it needs to add the number of piece equipment according to the regulations and maintenance efforts.Keywords: active fire protection system, dormitory, mapping
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".