Warenlagergebäude in Deutschland: Eine neue Methodik zur regionalen Quantifizierung der Flächeninanspruchnahme
Bibliographic record
Abstract
In 2018, the building segment of warehouse buildings accounted for around a quarter of all newly constructed floor space in German non-residential buildings. Despite this great significance, there is a lack of in-depth analyses that look at construction activity in a differentiated manner by region and sector. As a result, little is known so far about the land-use implications of construction in this building segment. This paper a methodology to quantify the land use of warehouse buildings on a regional scale is presented. Based on a data set from the German Research Data Centre for the years 2000 to 2015 and by applying GIS-supported conversion parameters, propositions on the small-scale characteristics of land use are possible down to the municipal level. It is apparent that the segment of warehouse buildings is characterized by concentration effects in three respects: the buildings are getting larger and larger, they are increasingly being constructed by specialized logistics service providers and are spatially concentrated in only a few well-connected locations. As a result, contrary to the general trend, the land use of this segment is steadily increasing. A differentiated consideration of land use along spatial and functional contexts seems to be advisable for the success of national land-use targets.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".