Structure Disentanglement and Effect Analysis of the Arid Riverscape Social-Ecological System Using a Network Approach
Bibliographic record
Abstract
Riverscapes are coupled social-ecological systems (SESs), in which the differences between the scales and functioning of interacting social and ecological components ( “mismatch”) impose challenges for global arid basin sustainability. Here, we defined riverscape SESs as networks of connected ecological and social components (nodes) to disentangle the structure and effects of SESs in Heihe River Basin (HRB) in arid regions of northwest China. Results showed the ecological network in HRB has low network density and high vertex strength. Heihe River Basin Bureau, as an emerging bridging organization, changed the SES structure and increased the matching degree of SES from 0.33 to 0.53, which has caused an obvious improvement in the downstream ecology. However, the characteristics of the ecological network demonstrated that cross-boundary management actions restricted to only the river would exacerbate local environmental pressures, such as the continued decline of groundwater in midstream regions and the potential appropriation of water for ecology by the expanding farmland in the downstream region. Our study demonstrated that network analysis could be one promising direction to untangle the complex SES and understand the relationship between SES structure and outcomes. We suggest comanaging the cross-boundary river and lands to further match the SES for basin sustainability.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".