Hybrid threshold-based distributed discovery service for the EPCglobal network
Bibliographic record
Abstract
In this paper, we present a new threshold-based discovery service for the EPCglobal network to be used by the Electronic Product Code-Border Gateway Protocol (EPC-BGP)- based discovery service. Our proposal employs hybrid threshold-based triggering and enables status updates in the routing tables based on either elapsed time or number of changes in the status of the EPC whereas the term threshold denotes the upper bound on the ratio of changes in the EPC status table of a supply chain. We evaluate our hybrid technique with respect to update blocking probability and define three types of blocking, namely the Justified Update Blocking (JUB), Unjustified Update Acceptance (UUA), and Unjustified Update Blocking (UUB). We investigate the impact of the frequency of update advertisements on the blocking probability. Through numerical results we show that the hybrid threshold-based update outperforms its counterparts in terms of all types of blocking probability. We further show that the proposed hybrid threshold-based update technique can control blocking probability under frequent product recall arrivals. Moreover, numerical results confirm that our proposal can adapt to traffic change and reduce the blocking probability significantly compared to its counterparts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".