Pick up points in e-commerce logistics, the emergence of two models
Bibliographic record
Abstract
In recent years, there has been a strong development of pick up points in urban and suburban areas in many European countries (particularly in France, Germany, the United Kingdom, and Benelux). Pick up points are local collection and distribution depots, or boxes, from which consumers can pick up goods they have ordered via home retail services (by mail order or order made on the internet). Their development has paralleled the rapid growth of sales made on the internet since years 2002-2003. In the last 5 to 6 years, the development of drop boxes and relay points has been remarkable in many European countries. In this article, we propose the identification. Avec le développement très rapide du commerce électronique ces dernières années, les consommateurs européens, notamment en France, en Allemagne, au Royaume-Uni et au Benelux, ont vu s’installer près de chez eux un nombre croissant de relais-livraison, en particulier en zones urbaines. Les relais-livraison sont des points de dépose et de collecte à partir desquels les clients de la vente à distance vont récupérer les colis commandés par internet ou par courrier. Ces relais remplacent la livraison à domicile, qui suppose une remise en main propre du colis par le livreur au destinataire. Dans cet article, nous recensons les expériences récentes de relais-livraison en Europe, en en étudiant les caractéristiques principales ainsi que les facteurs de réussite ou d’échec. Nous comparons en particulier l’émergence très récente des « consignes automatiques » au déploiement plus ancien (mais qui se fait aujourd’hui sous une forme modernisée) des « points-relais » hébergés dans les commerces de proximité. La présentation détaillée de quatre expériences (E-box, Kiala, Packstation et Cityssimo) nous permet de dresser des analyses plus précises sur le devenir de ces réseaux.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".