Estimation of flood risk management in 17th Century on Okayama Alluvial plain, Japan, by numerical flow simulation
Bibliographic record
Abstract
In this study, the hydraulic function of the Hyakken-gawa Floodway, which was constructed in the 17th century to reduce the flood risk to Okayama Castle City, was evaluated by numerical flow simulation.The calculation conditions were determined by referring to the records about the floodway in old documents as well as from the numerical data based on the present conditions.The alluvial plain topography used for the inundation calculation was obtained from recent GIS data.The computation results showed that the flood control function of the floodway was composed of two stages: (1) Just before inundation occurred in the castle city, river water flowed into the floodway by collapsing the earthen dike located at the floodway head; this limited the increase in the river flow rate in the city area.(2) Just before the floodway capacity was exhausted, the backwater generated by the transverse masonry dikes in the floodway induced inundation to the paddy field region, which was on the side opposite to the castle city; this reduced the flood damage in the city area.The results also suggested that the civil engineers in the 17th century, who had neither the knowledge of modern hydraulics (including the concept of river flow rates) nor the advantage of using machinery for construction, presumably developed flood risk management strategies by combining the measures possible in those days.Finally, the results indicate that the floodway design used to distinguish the paddy fields from the urban areas for flood acceptance can be utilized for risk management against extraordinary floods even in present times.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".