Border Security: CBP Lacks the Data Needed to Assess the FAST Program at U.S. Northern Border Ports
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "The United States and Canada share a border of nearly 5,525 miles. U.S. Customs and Border Protection (CBP), within the Department of Homeland Security (DHS), is responsible for securing the borders while facilitating trade and travel. CBP launched the Free and Secure Trade (FAST) program in 2002 to expedite processing for pre-vetted, low-risk shipments. GAO was requested to assess U.S.-Canadian border delays. This report addresses the following for U.S. northern border land ports of entry: (1) the extent to which wait times data are reliable and reported trends in wait times, (2) any actions CBP has taken to reduce wait times and any challenges that remain, and (3) the extent to which CBP and FAST participants experience the benefits of the FAST program. GAO analyzed CBP information and data on staffing, infrastructure, wait times, training, and the FAST program from 2003 through 2009 to analyze operations. GAO visited six northern border land ports, which were primarily selected based on commercial traffic volume. GAO interviewed importers, trade organizations, and border stakeholders. The results are not generalizable, but provide insights."
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".