Food and feed supply and waste disposal in the industrialising city of Vienna (1830–1913): a special focus on urban nitrogen flows
Bibliographic record
Abstract
Taking an urban metabolism perspective, this article investigates food and feed consumption as well as flows of nitrogen in the city of Vienna during the industrial transformation. It addresses the question of the amount of agricultural products consumed in the city and their nitrogen content, their origin and their fate after consumption. Changes in dietary nitrogen flows in nineteenth century Vienna are embedded in the context of a socio-ecological transition from an agrarian to an industrial socio-metabolic regime. Similarities and differences in the size and dynamics of urban nitrogen flows in Vienna and Paris are discussed. Critical reading of historical sources and historical material flow accounting are the methodological backbone of this study. Between 1830 and 1913, inflows of dietary nitrogen into the city increased fivefold. Throughout the time period under observation, the urban waterscape was the most important sink for human and animal excreta. The amount of nitrogen disposed of in the urban waterscape via urban excreta increased sevenfold. The average daily consumption of nitrogen per capita was very similar to that in Paris, but the composition of foodstuff differed. In Vienna, the share of meat in food consumption was considerably higher. Both cities had to face the challenge of increasing output flows. However, urban authorities in Vienna and Paris came to different solutions of how to deal with this challenge. Besides institutional settings, the specific geomorphology of the cities as well as biogeographic factors such as the absorption capacity of the Danube in Vienna and the Seine in Paris mattered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".