Bibliographic record
Abstract
These days there is a rapid increase in automobile utilization in urban and rural areas, along with this there is an increase in the number of accidents related to automobiles. Apart from user/ driver related accidents a large number of other reasons cause fires in automobiles. Three components are needed to make a fire, Oxygen, Fuel and a source of ignition. Car fires are usually caused due to issues associated with fuel, electrical systems, the exhaust system and petroleum based fluids. By far though, the biggest causes of vehicle fires are fuel (gasoline) related. The source of fire can be external or within the vehicle itself. Vehicle fires used to be quite common. Back in 1980’s there were 456,000 car fires. In 1978 a big issue occurred with Pintos catching on fire. This led the manufacturers to look at what design changes in vehicles will limit the three elements of the fire triangle from coming together. Our project aims to design a device which automatically detect fire in vehicles and suppress them to prevent further damage to the vehicle. The device which contains sensors and an extinguisher and a microprocessor can be placed under the hood of vehicles near the engine compartment and works when the engine (or any other part) catches fire. This application minimizes the possibility of death or injury and loss of property due to fire accidents in vehicles.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".