A Decision Tree Model for Evaluating Countermeasures to Secure Cargo at United States Southwestern Ports of Entry
Bibliographic record
Abstract
This paper presents a decision tree model for evaluating countermeasures to reduce vulnerabilities to terrorism in commercial truck crossings at United States southwestern land ports of entry. The model includes critical events in four phases of cargo movement: cargo transfer in Mexico, Mexican customs, U.S. customs, and the inland phase. Improvements in transportation security, inspections at Mexican ports, and at U.S. ports, are comparatively evaluated using parameterized variables. Costs and benefits of such improvements are analyzed to counter a radiological dispersion device (also known as a “dirty bomb”) attack. The results suggest that security decisions depend primarily on the probability of attack and parameters that influence the overall cost of false alarms. Extensive exploratory analysis reveals that improving security at Mexican ports is not recommended, mainly due to the cost of false alarms. However, a high percentage and a high cost of false alarms may justify new radiation portal monitors at U.S. ports even when improvements in the capability to detect dangerous cargo are insignificant. Better transportation security is not recommended if the probability of attack is less than 0.15. When the probability of attack exceeds 0.15 and false-alarm related costs are high, the model recommends enhancing transportation security. In addition, the parameters modeling economic consequences of an attack in a populated area, the probability of discovering weapons after smuggling into the United States, the probability of detonation, as well as the probability of detection at U.S. ports of entry, have significant impact on the countermeasure decision.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".