Expansion of flood control capacity in design standard of agricultural dam
Bibliographic record
Abstract
The Agricultural Production Infrastructure Design Standard has not been revised for more than 10 years, except the Drainage Part (2012), so it needs to be upgraded to respond to changes in the agricultural environment, such as climate change and agricultural system conversion.In many countries such as the United States, Europe, and Japan, standardized codes have been established for the maintenance of agricultural infrastructure.These codes reflect the latest research and technological trends, and they are updated regularly to ensure efficient operation and management.The need for the revision of agricultural infrastructure design standards is necessary to reflect the latest technologies for responding to agricultural disasters (disasters caused by climate change such as droughts and floods), and to strengthen the standards for expanding flood control capacity.This study aims to revise the overall contents of the design standards for agricultural dams such as climate change response, economic feasibility analysis, and new technology introduction, incorporating the deliberations of the Central Construction Deliberation Committee, the construction standards for emergency discharge facilities, smart management, the Huff method, and the AHP method for the economic analysis.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".