Bibliographic record
Abstract
Motivated by the waiting lines between the U.S.–Canadian border crossings, we investigate a security-check system with both security and customer service goals. In such a system, every customer has to be inspected by the first-stage inspector, but only a proportion of customers need to go through the second stage for further inspection. This “further inspection proportion,” affecting both security screening and the system congestion, becomes a key decision variable for the security-check system. Using a stylized two-stage queueing model, we established the convexity of the expected waiting cost function. With such a property, the optimal further inspection proportion can be determined to achieve the balance of the two goals and the service capacities can be classified into “security-favorable,” “security-unfavorable,” or “security-infeasible” categories. A specific capacity category implies if the security and customer service goals are consistent or in conflict. In addition, we have verified that the properties discovered in the stylized model also hold approximately in a more general multiserver setting. Numerical results are presented to demonstrate the accuracy and robustness of the approximations and the practical value of the model. This paper was accepted by Assaf Zeevi, stochastic models and simulation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".