Border Infrastructure Investment Plan: Canada - United States April 2013
Bibliographic record
Abstract
As part of the Action Plan, the two countries are to pursue a major new initiative relating to infrastructure proposed and developed by Transport Canada (TC), U.S. Department of Transportation (USDOT), Canada Border Services Agency (CBSA) and U.S. Customs and Border Protection (CBP). This initiative has two key elements. First, Canada and the U.S. “commit to make significant investments in physical infrastructure at key crossings to relieve congestion and speed the movement of traffic across the border.” The Action Plan listed the following as examples of significant infrastructure upgrades: “customs plaza replacement and redevelopment; additional primary inspection lanes and booths; expanded or new secondary inspection facilities; expanded or new connecting roads, highway interchanges and bridges.” These investments depend upon funding appropriations approved by the Canadian Parliament and the U.S. Congress. Nothing in the Action Plan and in this document is intended to give rise to rights or obligations under domestic or international law; neither the Action Plan nor this document are intended to constitute an international treaty under international law. CBP investments are to be delivered in partnership with the General Services Administration (GSA) which functions as CBP’s ports of entry Service Provider. \n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 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".