Marchandises en ville et logistique urbaine : de l'ignorance à l'action
Bibliographic record
Abstract
Freight transport in cities has long been one of the forgotten aspects of urban policies, but in the space of two decades they have become one of their most pressing questions. This rather obscure past has for its chief explanation the very limited quantitative knowledge which one could have of this activity. At best, it was suspected that this could contribute greatly to congestion through the lived experience of his car immobilized behind a vehicle in delivery. However, it was far from clear that these deliveries could, in certain districts and at certain hours, account for more than half of the determinants of congestion, as the investigations to which we are going to refer. More generally, it had to be recognized that in urban areas statistics and, a fortiori, modeling of the transport of goods had a considerable delay in relation to passenger transport. Indeed, in terms of the movement of people, there already existed a range of operational models based on a long-established formalization standard. These models naturally included statistics from travel surveys, which themselves were based on a standardized methodology since the 1970s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".