BikeBay: A Scalable AI Model for Bike Marketplace
Bibliographic record
Abstract
The rapid growth of online marketplaces has transformed the way consumers buy and sell vehicles and still most of the two-wheeler platforms rely on manual listings, keyword-based search and static pricing methods. This can induce poor search accuracy, inconsistent pricing and increase the vulnerability to fraudulent activities. To solve these challenges, this paper proposes - BikeBay, an AI-powered two-wheeler marketplace that uses computer vision, predictive analytics and fraud detection to automate and to enhance trust and efficiency in the resale ecosystem. The proposed system implements Gemini Vision AI for image-based two-wheeler recognition and automatic specification extraction, a machine learning regression model for price estimation and an AI-powered detection module that identifies inconsistency in listings and prevents duplicate or mismatched entries. The backend is built on Node.js with PostgreSQL via Prisma, while the frontend is implemented with React.js and tailwind css , which makes sure scalability and user-friendly interactions on the platform. The research work shows how BikeBay uniquely varies from existing platforms by offering AI-driven automation, intelligent pricing and fraud prevention tailored specifically for two-wheelers. This research work presents to the growing field of AI-enabled marketplaces and provides a scalable framework that can be extended to include insurance, financing and EV-specific marketplaces in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".