E-Business Challenges for Intermodal Freight: Some International Comparisons
Bibliographic record
Abstract
Separate Canadian and Australian government-sponsored studies, both reporting in mid-2001, have investigated expected impacts of rapid growth in e-business on transportation infrastructure and services. The different viewpoints of these two studies allow consideration of implications for freight transportation in general and intermodal freight in particular. The findings reported, relating to supply chain changes, special challenges for rail- and sea-freight carriers, and changes in requirements for freight warehousing and interchange, have international relevance, which leads to suggested policy responses. The importance of e-business in affecting freight transportation should not be underestimated. Transportation experts interviewed in the Australian study unanimously considered this the issue of most consequence for transportation over the next 5 to 10 years. Both studies point to freight transportation having significant potential to aid economic growth from e-business and conversely to a lack of appropriate infrastructure to accommodate that growth.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".