Is RFID the Answer to Resurgent Border Traffic?
Bibliographic record
Abstract
With respect to cross-border passenger travel at Blaine, Washington (the I-5 corridor), two things were evident in the aftermath of 9/11—the volume of travel dropped dramatically, and the at-booth inspection process became more time-consuming. The combined effect was that wait-times remained roughly comparable to what existed pre-9/11, despite traffic volumes that were 25 percent lower. The constant worry, though, was “How will we cope when traffic volumes climb?” For eight years regional stakeholders pursued initiatives intended to reduce wait-times, even as traffic volumes languished at an average volume of about 215,000 cars per month. The tail end of that eight-year period is seen in the left half of Figure 1, and the long-anticipated resurgence of traffic is evident in the right half, dating roughly from autumn 2009. In the first ten months of 2011 (highlighted in the figure), traffic reached an average level of 358,000 cars per month, a level not observed since 1997. The vast majority of those travelers were Canadian residents.
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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".