TREND: Bureau of Transportation Statistics. Border Crossings: Border Crossings - Train Passengers | State: Arizona | Port: AZ:Douglas, AZ:Lukeville, AZ:Naco, AZ:Nogales, AZ:San Luis, AZ:Sasabe, 1996 - 2014. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 007-003-016
Bibliographic record
Abstract
Bureau of Transportation Statistics. Border Crossings: Border Crossings - Train Passengers | State: Arizona | Port: AZ:Douglas, AZ:Lukeville, AZ:Naco, AZ:Nogales, AZ:San Luis, AZ:Sasabe, 1996 - 2014. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 007-003-016 Dataset: Number of entries of train passengers and crew into the US at 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. http://www.bts.gov/programs/international/transborder/TBDR_BC/TBDR_BC_Index.html Category: Transportation and Traffic, Military and Defense Subject: Passengers, Rail Transportation, Border Crossings, Border Patrol, Transportation, Homeland Security, Railroads 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. http://www.bts.gov/
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.079 |
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".