Lelantos: A Blockchain-based Anonymous Physical Delivery System.
Bibliographic record
Abstract
Real world physical shopping offers customers the privilege of maintaining their privacy by giving them the option of using cash, and thus providing no personal information such as their names and home addresses. On the contrary, electronic shopping mandates the use of all sorts of personally identifiable information for both billing and shipping purposes. Cryptocurrencies such as Bitcoin have created a stimulated growth in private billing by enabling pseudonymous payments. However, the anonymous delivery of the purchased physical goods is still an open research problem. In this work, we present a blockchain-based physical delivery system called Lelantos1 that within a realistic threat model, offers customer anonymity, fair exchange and merchant-customer unlinkability. Our system is inspired by the onion routing techniques which are used to achieve anonymous message delivery. Additionally, Lelantos relies on the decentralization and pseudonymity of the blockchain to enable pseudonymity that is hard to compromise, and the distributed consensus mechanisms provided by smart contracts to enforce fair irrefutable transactions between distrustful contractual parties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".