Bibliographic record
Abstract
Logistics sprawl is a land use phenomenon, which has been detected in many urban areas around the world (e. g. Paris, Atlanta, Toronto, Los Angeles, and others). It consists in the displacement of logistics sites from urban areas to suburban areas. Logistics sprawl can negatively affect the reliability of deliveries, increase delivery costs and have undesirable environmental effects (emissions, land consumption). Over the last 20 years, Zurich experienced a significant densification of its urban fabric, which potentially fostered the sprawl of the land-intensive logistic sector. Therefore, we analysed the locations of four types of logistic firms (road transportation, storage, courier service, postal service) in the Zurich area in the period of 1995 – 2012 to determine to what degree logistic sprawl occurred in said area and how this affected the distance of the mean delivery route. The analysis shows that the mean distance of the storage and the courier services firms from the Zurich city centre increased significantly. The logistic sprawl did not occur for the road transportation firms and for post offices. To avoid the displacement of logistics firms in suboptimal sites, it is important to proactively locate the best potential logistics sites and secure them by land use regulation.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".