Sustainability Evaluation on the Grain to Green Program in the Hexi Corridor of China: A Metacoupled System Perspective
Bibliographic record
Abstract
Most previous studies aim to predict ecosystem sustainability from the perspective of a sole human or natural system and have frequently failed to achieve their desired outcome. Based on the coupled human and natural system (CHANS) and its interaction with other systems, we attempted to analyze the effectiveness of the Grain to Green Program and predict future trends in the Hexi Corridor, the hub of the ancient silk road of China. At different scales, we applied a metacoupling framework to investigate the flows, effects, and causes of the complex CHANS. Three typical inner river watersheds within the corridor at three different geographic scales (local, regional and national) were estimated and compared. The Telecoupling Geo App, additional models, and software tools were employed to evaluate the CHANS series of the focal system (Hexi Corridor, local), adjacent system (Gansu Province, regional), and distant system (China, national). The results showed that most flows can be screened and quantitatively analyzed across focal, adjacent and distant systems. The social and economic transformations in adjacent and distant systems could affect the possibility and whereabouts of labor transfer in the focal system. Moreover, the labor migration increased the implementation efficiency of the Grain to Green Program as a Payment for Ecosystem Services (PES) strategy, thereby improving its ecological benefits. For the first time, we established a metacoupled model to quantitatively evaluate aspects of ecosystem sustainability in China, providing insight to the theory and application of sustainability science.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".