SMART INTELLIGENT CONGESTION CONTROL AMBULANCE ASSISTANCE SYSTEM USING WIRELESS COMMUNICATION TECHNIQUES
Bibliographic record
Abstract
The proposed method aims in designing a system which is capable identifying the emergency situation in ambulance and automatically controls the traffic at the signal point. This feature helps in decreasing the death rate which occurs mostly due to traffic congestion in emergency situation. Traffic density sensor is used to increase the green time when the traffic is heavy. Zigbee is used for establishing wireless communication between system in ambulance and the system at signal point, few control buttons in ambulance are used for indicating emergency situation, and traffic signal indicators are to be controlled depending on emergency situation in the ambulance. In Zigbee transmitter, which is their in the ambulance and placed four buttons i.e. east, west, north and south. The driver presses either of these button depending on the ambulance direction. So, this transmitter sends signal to that Zigbee receiver placed at the traffic junction and the PIC microcontroller takes the action of controlling the LEDs and IR obstacle sensor identify the heavy traffic.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".