E-commerce trends and implications for urban logistics. In, Browne, M., Behrends, S., Woxenius, J., Giuliano, G., Holguin-Veras, J. Urban logistics. Management, policy and innovation in a rapidly changing environment
Bibliographic record
Abstract
This chapter describes the changes in freight and logistics induced by the rapid development of e-commerce consumption, and when possible quantifies them. Data on e-commerce induced urban freight traffic are still in very poor supply. The first simple information on the number of urban deliveries and pick-ups related to e-commerce, for example, is not easy to provide. Because of its crucial importance to understand trends, the availability of data will be the focus of section 2. The rest of the sections will look at the main areas of change: in section 3, consumers' new behaviours; in section 4 the supply of delivery services; in section 5, urban freight innovations related to e-commerce; in section 6 a focus on 'instant deliveries;' and in section 7 the impact of e-commerce on urban warehousing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".