Local Hive: A Blockchain-Enabled Microservices Platform for AI-Driven On- Demand Local Service Aggregation
Bibliographic record
Abstract
Because there is a lack of trust, transparency, and efficient provider verification, the fragmented local service discovery landscape results in inconsistent service delivery and user dissatisfaction. In this paper, we present Local Hive, a block-chain-enabled micro services platform that uses decentralized architecture and AI-driven overspecialization to link users with verified local service providers. To guarantee safe, instantaneous service matching, the platform integrates context-aware computing, geofencing, and homomorphic encryption. A $92 \%$ payment success rate, an $85 \%$ user satisfaction rate, and a $65 \%$ decrease in booking latency were all shown in pilot testing involving 150 users and 75 service providers. The Apache Kafka-based event-driven architecture achieved 99.2% system availability under variable load conditions. AI-powered suggestions raised booking conversion rates by $43 \%$, and Local Hive’s block-chain-based provider verification raised user trust by $78 \%$ when compared to traditional platforms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".