Feeding and cleaning the city: the role of the urban waterscape in provision and disposal in Vienna during the industrial transformation
Bibliographic record
Abstract
This article presents an integrated socio-ecological perspective on the changing interrelations between Vienna’s “urban metabolism” and the river Danube during the industrial transformation in the nineteenth century. During this period of rapid urban population growth and industrial development, the amount of materials and energy used in the city as well as the corresponding outflows of wastes and emissions, that is, the size of urban metabolism, multiplied. These changes in urban metabolism had a profound effect on the relation between city and river. The paper explores the changing role of the Danube and its waterscape for urban supply and urban discharge in the period from 1800 to 1910. It presents quantitative information on urban resource supply and river transport and discusses the changing function of the river as a major transport route. It investigates urban discharge of waste water and the evolution of a sewer system and discusses how the changing waterscape was reflected in perception and discourse. We find that there was a qualitative change in the transport function of the river. While the river lost importance in the provision of the city with energy it remained crucial for the supply of cereals. Furthermore we observe a general shift from the significance of the river in supplying the city towards the river’s function for the disposal of waste.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".