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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".