Distributed discovery services via EPC-BGP for mobile RFID
Bibliographic record
Abstract
In this paper, we propose an extended architecture of the EPCglobal network that allows tracking objects. This architecture makes use of the distributed discovery services along with the EPC-BGP to provide detailed information about an object regardless of its location. In the EPCglobal network, each object is assigned an IPv6 address once it leaves the last gateway in the supply chain. The IP address of the last gateway enables backtracking of all the information about this object throughout the supply chain. To this end, EPC status updates are crucial in order to advertise any changes in the EPC into the supply chain. On the other hand, concurrent EPC updates, expired EPC databases and/or limitation of resources may cause blocking of an EPC update request. Therefore, we evaluate our proposed architecture in terms of blocking probability of the EPC update requests. To this end, We 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. Numerical results confirm the trade-off between blocking probability and communication/computation overhead due to EPC update messages. However, further investigation in terms of the number of advertisements and the distance between the routing tables confirms that advertisement of EPC update messages based on certain thresholds can overcome this trade-off.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".