Biometric Authentication and Identification Systems for Border Controls: A look at U.S. and Canadian Programs
Bibliographic record
Abstract
Numerous media reports over the past couple of years have highlighted recent U.S. and Canadian efforts to strengthen border security. 1 These efforts have involved the implementation of numerous new initiatives under an overarching bi-lateral agreement known as the Smart Border declaration. 2 Central to the Smart Border agreement is the use of biometric identifiers. Most U.S. initiatives are part of a program known as USVISIT (United States Visitor and Immigrant Status Indicator Technology) while Canada has a number of smaller programs. When fully implemented the USVISIT program will see the most expansive use of biometrics as a security tool in the world, utilizing integrated hardware, databases, enforcement measures, and other tools. 3 Fundamental to both Canadian and U.S. programs is the use of biometric identifiers in new machine-readable “smart ” travel documents such as visas, permanent resident cards, and passports. The implementation timetable for these programs was considerably accelerated in the days following the September 11, 2001 terrorist attacks. Current biometric technology, while useful in many aspects of border security offers only very specific and limited assistance to anti-terrorist activities. Thus the vast
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".