An Evaluation of and Proposed Solution to the Need for Shared Transportation Data at the United States-Canada Border
Bibliographic record
Abstract
Many stakeholders conduct operations at land ports of entry along the border between the United States and Canada. Port administrators, state and provincial agencies, national bodies, and private firms all have a stake in border operations would benefit from access to statistical data on border operations to conduct their business. However, due to multiple factors, the relevant data are often not readily available to the stakeholders for use. Some border and transportation data-sharing mechanisms do exist; while helpful, none of them address the needs of all interested stakeholders. Therefore a better mechanism for sharing of border-data information is needed. This paper describes the creation, implementation, and evaluation of one potential solution to this problem. Key principles of database publishing were used to create an internet-based data repository (http://128.95.204.38/erica/ibid/) comprised of twenty-eight individual data sources. The website interface allows searching of sources by jurisdiction, data type, or custom search strings. A cross-section of stakeholders was selected to test the website, and results suggest it will be useful in routine practice.
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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 | 0.048 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".