Bibliographic record
Abstract
With the expansion of urbanization in today's world, transportation plays a crucial role in sustainable urban development.Meanwhile, with the advancement of technology and its significant impact on improving human life, smart transportation systems are one of the requirements of urban development and of the infrastructure of a smart city.These increasingly expanding and developing systems with their technological base increase productivity and safety of transportation.As such, the provision of a suitable place for car parks is one of the essential needs of a developed city.On the other hand, with the increasing number of cars and consequently the size of parking slots in metropolises, it is impossible to manage them without the use of new systems in practice.In this article, we will design and construct an on-street smart parking system based on GSM, GPS, and Google Maps using the Android app.One of the special features of this project is the online booking system and paying the cost of parking, as well as displaying road surface temperature information using the Android app, which will improve productivity.In addition to other benefits, a quick and accurate diagnosis of occupancy or being available of parking space in a practical implementation indicates the capability and proper efficiency of the proposed system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.952 | 0.953 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".