Bibliographic record
Abstract
Patterns of freight movement result from economic exchange. That is, buying and selling activities along complex global supply chains drive infrastructure-level demand. Yet commodity forecasting techniques used in freight plans and port studies frequently ignore these broad drivers. The fundamental thesis of this paper is that the macro level—the global macroeconomic perspective—is increasingly important to making reasonable freight projections at the micro level—the infrastructure level. This paper therefore has two goals. First, it briefly reviews the state of the practice in freight forecasting and ultimately concludes that the macro level is noticeably absent in most forecasting methods and reviewed studies. Second, the paper presents a method of generating county-level commodity forecasts that embody macro drivers and trends. Specifically, the approach ties together three critical pieces of information: (a) a county-based social accounting structure representing detailed factors of economic supply and demand; (b) a set of domestic macroeconomic forecasts providing future industry-by-industry production trends that recognizes spatial growth patterns, changing technology, relative industry growth, and broad forces affecting final demand; and (c) a forecast of U.S. international trade, recognizing differential economic growth of trading partners as well as pressures from international competition and currency fluctuations. The result of this methodology is county-level trade forecasts (in dollars) that are analytically (not statistically) tied to macroeconomic growth trends. These forecasts can be used alone for sketch or policy-level analysis, or they can be combined with meso- and micro-level information and models for comprehensive freight forecasting at the infrastructure level. The method presented is being implemented in the Transportation Economic Development Impact System, a web-based analysis system used in planning major transportation investments in the United States and Canada.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".