Understand systemic risk from mangrove ecosystem through network analysis
Bibliographic record
Abstract
Mangrove deforestation amplifies systemic risks by worsening extreme weather events, impeding socio-economic development, and exposing governance vulnerabilities. Yet, the extent to which mangrove dynamics-both loss and restoration-interact with climate, socio-economic, and governance systems to mitigate systemic risk remains underexplored. Drawing on the economic concept of "product space," we construct a Mangrove Multisystemic Risk Space, a network-based framework linking indicators across mangrove change, climate impacts, socio-economic development, and policy interventions. The network reveals a bipartite structure, with distinct clusters for mangrove loss and expansion, each surrounded by synergistic indicators. The mangrove loss cluster is tightly coupled with greenhouse gas emissions and climate extremes, while the expansion cluster aligns with renewable energy, economic growth, and population dynamics. Within this space, we identify two types of structurally significant indicators: "influential" (e.g., Ramsar site coverage) with high cascading potential, and "complex" indicators that require coordinated improvements across multiple dimensions, highlighting their systemic vulnerability. At the national level, the United States leads in achieving complex goals such as reducing extreme events, whereas New Zealand and Panama emerge as hubs of influential, well-performing indicators. These findings underscore the differentiated roles of mangrove-rich nations in mitigating systemic risk and call for strengthened global cooperation in mangrove conservation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".