Blue-green water migration and utilization efficiency under various irrigation-drainage measures applied to a paddy field
Bibliographic record
Abstract
The study area was Nanjing, Jiangsu Province, eastern China. Blue-green water utility assessment in crop cultivation is profound to better sustainability of regional resources, agriculture and ecosystems. The aim of current study is to establish a blue-green water differentiation and efficiency evaluation methods for paddy fields, based on daily water migration observation under four irrigation-drainage protocols. The blue-green water use performance under different irrigation-drainage protocols and precipitation patterns in paddy fields and the advantage of the water resources analysis model proposed in the current study were analyzed and discussed. The green and blue water efficiency indices (GWE and BWE) were significantly affected by precipitation. GWE (0.495) was 35.1% higher in dry years than in wet years, and BWE was mainly affected by precipitation distribution during the crop growth season. The green and blue water productivity indices (GWP and BWP) showed pronounced differences among various irrigation-drainage measures, and the controlled irrigation (COI) performs the best. BWE and GWP obtained by previous method were 48.9% and 38.9% greater than those reported here, showing that the neglect of interactive blue-green water migration process would overestimate the field irrigation efficiency. The methods employed and results obtained in the present study suggest that it is important to assess the water footprint and blue-green performance for crop cultivation systems.
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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 | 0.000 | 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 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".