The Freight Analysis Framework Verson 4 (FAF4) - Building the FAF4 Regional Database: Data Sources and Estimation Methodologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns the flows to the transportation network, and projects freight flow patterns into the future. The FAF4 is the fourth database of its kind, FAF1 provided estimates for truck, rail, and water tonnage for calendar year 1998, FAF2 provided a more complete picture based on the 2002 Commodity Flow Survey (CFS) and FAF3 made further improvements building on the 2007 CFS. Since the first FAF effort, a number of changes in both data sources and products have taken place. The FAF4 flow matrix described in this report is used as the base-year data to forecast future freight activities, projecting shipment weights and values from year 2020 to 2045 in five-year intervals. It also provides the basis for annual estimates to the FAF4 flow matrix, aiming to provide users with the timeliest data. Furthermore, FAF4 truck freight is routed on the national highway network to produce the FAF4 network database and flow assignments for truck. This report details the data sources and methodologies applied to develop the base year 2012 FAF4 database. An overview of the FAF4 components is briefly discussed in Section 2. Effects on FAF4 from the changes in the 2012 CFS are highlighted in Section 3. Section 4 provides a general discussion on the process used in filling data gaps within the domestic CFS matrix, specifically on the estimation of CFS suppressed/unpublished cells. Over a dozen CFS OOS components of FAF4 are then addressed in Section 5 through Section 11 of this report. This includes discussions of farm-based agricultural shipments in Section 5, shipments from fishery and logging sectors in Section 6. Shipments of municipal solid wastes and debris from construction and demolition activities are covered in Section 7. Movements involving OOS industry sectors on Retail, Services, and Household/Business Moves are addressed in Section 8. Flows of OOS commodity on crude petroleum and natural gas are presented in Sections 9 and 10, respectively. Discussions regarding shipments of foreign trade, including trade with Canada/Mexico, international airfreight, and waterborne foreign trade, are then discussed in Section 11. Several appendices are also provided at the end of this report to offer additional information.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it