Changes in water and land: the reconstructed Viennese riverscape from 1500 to the present
Bibliographic record
Abstract
Medieval Vienna was situated at the main arm of the swiftly flowing alpine Danube. From the fourteenth century onwards, the river gradually moved away from the city. This marked the beginning of 500 years of human intervention to prevent further displacement of the river and to preserve the waterway as a vital supply line. Archival research and the GIS-based reconstruction of the past riverscape allow a new view about the co-evolution of the city and the river. Following major channel changes in 1565/1566, repeated attempts to force the main arm into the old river bed were undertaken. By the early seventeenth century, the Viennese had accepted the new situation. Resources were now spent on maintaining the waterway to the city via the remaining Wiener arm . After the second Ottoman siege in 1683, improving the navigability of the Wiener arm , in conjunction with major expansions of the fortifications, became the main issue. Between 1775 and 1792, the first systematic, effective flood protection measures were established. These substantially influenced fluvial dynamics and enabled urban development in parts of the former floodplain. The all-embracing transformation of the dynamic riverscape into stabilised areas enabling urban growth and secure waterways was not achieved until 1875. With this successful “re-invention” of the Viennese Danube, an irreversible path was struck in the common life of the city and the river, a path which is still decisive for the interaction of Vienna with that great European river.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".