Addressing supply-chain complexity using closed-loop simulation-based exercises
Bibliographic record
Abstract
From news reports about companies attempting to reduce the impact of compromised supply chains, due to natural disasters, accidents or targeted attacks, or trying to avoid specific products or ingredients banned on moral grounds, it is apparent that many organizations have only rudimentary knowledge of the provenance of software, hardware, and other supplied items.Reasons for this situation include the difficulty and effort required to:• build and maintain complete and accurate databases; • obtain information on subcontractors down to the required level of detail; • review, monitor and test products to ensure that they are genuine; • encourage eradication of deficiencies, weaknesses, and vulnerabilities; • ensure that changes are identified, reported, analyzed, and addressed; • identify commonalities and common points of failure; • introduce resiliency, redundancy, and backup within the supply chain; • develop methods to simulate infrastructures, transactions, etc.; and • bring together competitors to collaborate in exercising various scenarios.Thus, the question arises as to how to resolve these issues in an accurate, efficient, and cost-effective manner.Answering this question is our goal.Supply-chain models are generally substantially more intricate than the model developed for the US equities marketplace.However, the same approach works for developing and operating any complex industry-wide and sector-wide systems with many participants who want to keep proprietary information confidential but need to share information to facilitate a rich exercise experience for learning, training, and testing a variety of realistic scenarios.This paper describes a process for implementing such simulation-based exercises.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".