TREND: Bureau of Transportation Statistics. Border Crossings: Border Crossings - All Vehicles, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-001Bureau of Transportation Statistics. Border Crossings: Border Crossings - All Incoming People, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-002Bureau of Transportation Statistics. Border Crossings: Border Crossings - Loaded Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-003Bureau of Transportation Statistics. Border Crossings: Border Crossings - Empty Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-004Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trucks, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-005Bureau of Transportation Statistics. Border Crossings: Border Crossings - Trains, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-006Bureau of Transportation Statistics. Border Crossings: Border Crossings-Loaded Truck Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-007Bureau of Transportation Statistics. Border Crossings: Border Crossings - Empty Truck Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-008Bureau of Transportation Statistics. Border Crossings: Border Crossings - Loaded Rail Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-009Bureau of Transportation Statistics. Border Crossings: Border Crossings - Empty Rail Containers, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-010Bureau of Transportation Statistics. Border Crossings: Border Crossings - Personal Vehicles, 01/1996 - 12/2016. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 007-003-011
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@24a Dataset: Number of entries of motor vehicles into the US through land ports along the US-Canadian and U.S.-Mexican border. This dataset contains data on entries into the US of vehicles, commercial containers, passengers, and pedestrians through land ports along the US-Canadian and U.S.-Mexican border. The Bureau of Transportation Statistics obtains this data on a monthly basis from U.S. Customs and Border Protection. https://transborder.bts.gov/programs/international/transborder/TBDR_BC/TBDR_BC_Index.html Category: Transportation and Traffic Subject: International Trade, Border Patrol, Transportation, Homeland Security, Border Crossings Source: Bureau of Transportation Statistics The Bureau of Transportation Statistics (BTS) was established as a statistical agency in 1992. The Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 created BTS to administer data collection, analysis, and reporting and to ensure the most cost-effective use of transportation-monitoring resources. BTS brings a greater degree of coordination, comparability, and quality standards to transportation data, and facilitates in the closing of important data gaps. https://www.bts.gov/
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.300 | 0.357 |
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".