La Métropole Logistique : Structure métropolitaine et enjeux d'aménagement La dualisation des espaces logistiques métropolitains
Bibliographic record
Abstract
Among other activities, metropolitan areas have become places of premium location for logistics activities. As a consequence of the concentration of warehouses in metropolitan areas, logistics facilities are mainly located in suburban areas, inducing logistics metropolization. This logistics suburbanization amplifies the negative externalities of transport and challenges public policies. However, suburban areas are not the only location choices of logistics facilities. Analysis on logistics sprawl should not overlook logistics facilities located in dense parts of metropolitan areas which, moreover, draw the focus of public authorities. The apparent contradiction between logistics that contribute to urban sprawl and the new sustainability goals has led to refocusing the debate on the "last mile" rather than logistical planning in the fringes of metropolitan area. Through the development of "urban logistics" policies, public stakeholders intend to offer a complementary service to those offered by the logistics real estate market, while complying with environmental objectives. The main challenge of analyzing this logistics metropolization lies in the double contribution of logistics to metropolitan morphology and the political agenda.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".