SOCIALIZATION OF FIRE MITIGATION IN DENSELY POPULATED AREA AT JATI SAMPURNA, BEKASI
Bibliographic record
Abstract
During the first quarter of 2019, from January to March 2019 there were 45 fire incidents in the city of Bekasi with losses ranging from Rp. 2,365,000,000 (based on data from the Bekasi City fire dept Service). From 45 events in the city of Bekasi, 3 of them occurred in Jatisampurna sub-district. Based on the type of object 45 events 15 of which are residential houses.With the data above, it can be said that the risk of fire can cause material and immaterial losses and can also cause trauma to fire victims. The importance of knowledge and information on fire hazard mitigation and how mitigation measures against fire hazards need to be conveyed to people who live in densely populated areas where the risk of fire is quite large. Laws or regulations on fire are not yet widely owned by most regions, so there are no special rules that can be covered in relation to fire risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".