Benchmark for supply chain monitoring properties using BeepBeep
Bibliographic record
Abstract
This is an instance of the LabPal experimental environment to benchmark the execution of processor chains for the BeepBeep event stream engine. The considered use case is related to the concept of hyperconnected logistics. In this model, the entire world can be split at the smallest scale into unit zones, whose size depends on expected demand density. Adjacent unit zones are grouped into local cells, which in turn are gathered into areas, which form regions. Simultaneously, several hub networks are defined to link these different layers: access hubs link unit zones together; local hubs link local cells, and gateway hubs link areas. Different hub levels may exist inside the same physical entity (e.g., a local hub might also be an access hub), thus allowing interactions between the different layers. In a recent work (see citation below), the authors showed how to concretely adapt a hyperlogistics simulation in order to integrate an Ethereum blockchain backend, in such a way that every action made by carriers is publicly stored in transactions on the blockchain itself. The combination of such simulation and blockchain backend allowed us to generate the traces used to monitor a number of properties. The experiments in this benchmark measure the throughput of a variety of BeepBeep processor chains on simulated logs of blockchain events generated on the fly.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".