A REVIEW OF SOME CRITICAL ASSUMPTIONS IN THE RELATIONSHIP BETWEEN ECONOMIC ACTIVITY AND FREIGHT TRANSPORT
Bibliographic record
Abstract
A number of conversion factors are often needed when projecting freight transport growth, depending on the level of detail of the projection. Here we investigate conversion factors that convert production in fixed prices in different industries into production of different commodities and further into weight terms. Data to describe these conversions are hard to come by and modellers have been left to resort to various ad hoc assumptions. We have obtained a data set covering the period from 1981 to 1992 detailing production by industry and commodity both in fixed prices and in tons based on the Danish national accounts. With these data we are able to check some of the assumptions that have commonly been made. Our findings thus have implications for future freight modelling exercises, in particular for what data it is necessary to collect and what relationships it is necessary to seek to model explicitly. We find that it is necessary to account for changing composition of production across industries, but that the commodity mix within each industry safely can be regarded as constant. Changing value densities account for almost a third of transport growth; however, this is attributable to the first year of data. Otherwise, value densities could be regarded as constant with our data. Finally, we find that using import or export data to impute value densities induces unacceptably large errors.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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; both teacher heads agree on what is shown here.
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".