North American Transportation Atlas Data (NORTAD): 1998
Bibliographic record
Abstract
The North American Transportation Atlas Data - 1998 (NORTAD) is a set of geographic data sets for transportation facilities in Canada, Mexico, and the United States. These data sets include geospatial information for transportation modal networks and intermodal terminals, and related attribute information. Included are descriptions of the file formats and metadata as prescribed by the Federal Geographic Data Committee (FGDC). The data on this compact disc (CD-ROM) support research, analysis, and decision making across all modes of transportation. The data are most useful at the national level, but have major applications at regional, state, and local scale throughout the transportation community. These data sets do not provide explicit connections between modes and terminals. This product is distributed in shapefile format. The shapefile is an open format created by the Environmental Systems Research Institute (ESRI). You can find additional information about the format at: www.esri.com/library/whitepapers/pdfs/shapefile.pdf The NTAD1998 databases are designed for use within a geographic information system (GIS); however, the attribute data for each dataset can be accessed in any database, spreadsheet, or other software package. This information is stored in dBASE format. Because of spreadsheet limitations, many of the larger dBASE files will not open correctly with spreadsheet software. It is important to note that users who manipulate attribute data outside of their GIS may alter the shapfiles' linkage between the attribute and spatial data.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.040 |
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".