Research on Risk-based Passenger Differentiation Techniques Applied to Aviation Security Screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the WHO declaring that the COVID-19 no longer constitutes a public health emergency of international concern, the civil aviation industry is poised for a retaliatory rebound in the volume of air passenger numbers. In order to ease the pressure of airport security screening, advanced technologies have been applied in many airports. However, most of these technologies focus on the prohibited items that passengers carry rather than the passengers who carry these items. The optimized utilization of advanced technologies requires risk-based passenger differentiation. This approach includes development of a risk score through behavior-based and data-based differentiation, assignment of the risk score to an individual passenger and incorporation of the score into the security screening process. As most of these techniques are carried out behind-the-scenes, or in a covert and stand-off way, security is enhanced without compromising most passengers’ seamless travel through the airport, and therefore achieve the balance between facilitation and security.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it