Bibliographic record
Abstract
In the present day, there are fire accidents are increasing rapidly, it becomes very hard for a fireman to save someone’s life. We cannot appoint a person on that accidental place to continuously observe for an accidental fire where the robot can do it very easily. Therefore, in such situation robot comes in picture. Fire fighting robot will detect fire remotely. These robots are mostly useful for industries level where the possibility of accidental fire is more. The Fire fighting robot can detect fire and controlling it automatically by using the gas sensor and temperature sensor. It has gear motors and motor drivers for controlling the movement of the robot. A relay circuit is used to control the pump and when the robot will detect fire then the robot will communicate with the microcontroller (Arduino UNO R3) via Bluetooth module. The fire fighting robot has a water jet spray that is used to sprinkling water. The sprinkler will move easily towards the required direction. At that time if some obstacle will detect when the robot will be moving towards the source of fire, then the robot has capable of avoiding an obstacle. It will provide a Graphical User Interface for Arduino operation using android. It detects obstacles by using ultrasonic sensors up to a range of 80 m. Communication between the robot and Mobile phone will take place via Bluetooth, which has a GUI to control the movement of the robot. When mobile gets connected to the Mobile phone through Bluetooth after that it will set the module name, baud rate. It is feasible to implement Bluetooth communication between Mobile phones and microcontrollers. Android controlled robots can be used easily in today’s busy life such as in the market, using for controlling home systems, companies, etc. Nowadays, the development of apps for Android in Android SDK is easy without any cost.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".