Assessment of Anti-Drought Capacity for Agricultural Reservoirs using RCP Scenarios
Bibliographic record
Abstract
Agriculture is affected directly by climate conditions and changes, and it is necessary to understand the impact of climate change on agricultural reservoirs which are the main water resources for paddy fields in Korea. This study aimed to evaluate the impact of climate change on the anti-drought capacity including water supply capability (WSC) and drought response ability (DRA) of agricultural reservoirs based on RCP (Representative Concentration Pathway) 4.5 and 8.5 scenarios of CanESM2 (The Second Generation Earth System Model) provided by CCCma (Canadian Center for Climate Modeling and Analysis). The WSC and DRA were estimated using frequency analysis and runs theory. The six reservoirs (Yooshin, Nogok, Kumsung, Songgok, Gapyung, Seoma) were selected considering geographical characteristics and design criteria of reservoir capacity. In case of Seoma reservoir, more than 10 year drought return period (DRP), the variation of the WSC was estimated larger than the others. In case of Yooshin reservior (2~5 DRP) DRC was decreased in 2025s under RCP8.5. These results could be utilized for agricultural reservoirs management and future design criteria considering climate change impacts on paddy irrigation.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".