A study of economic efficiency in port security inspection
Bibliographic record
Abstract
SUMMARY The purpose of this paper is to study an economic efficiency for the prediction of additional truck turnaround time and for determining the number of the port security inspection equipment required at a terminal inspection station. The economic efficiency with six base models was developed to assist terminal operators' decision‐making. In addition, this study developed an optimal procedure that terminal operators could use to optimally run in terms of various statistics processes including exponential, deterministic, and others, seeking a solution that was beneficial for both terminal operators and truckers. As a result of this research of the additional cargo turnaround time for port security delay, the following conclusion can be drawn and made. The average additional delay time in the inspection station is very dependent on the inspection rate of the lower stage. The higher weighted inspection time based on raising security level allows less number of trucks to be inspected, which will derive high delay in the inspection station. Increase of rate of Green Lane usage will allow a decrease in the arrival rate, which may derive improvement of inspection equipment efficiency and average delay time at the inspection station. In multiple stage model, total number of trucks and delay time very closely follow those of low inspection stage rate and number of inspection units. Free Lane is to be followed by Customs‐Trade Partnership Against Terrorism and standardization of customs, packing, loading and unloading, documents, procedure, and exchange working in each country. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".