Interdependencies among hydro-saline dynamics, economic activities, ecological processes, and biodiversity in a deltaic social-ecological system: insights from the Rhône delta (southern France)
Bibliographic record
Abstract
Deltas are complex social-ecological systems (SESs) that involve many economic activities that interconnect closely with ecological processes and biodiversity. Sustainable management of such intricate SESs requires developing an integrated and understandable representation of their main human and ecological entities, processes, and interactions. We developed a formal representation of the Ile de Camargue (center of the Rhône delta), in which hydro-saline dynamics, water management, economic activities (e.g., agriculture, hunting, fishing, tourism), and natural environments (i.e., habitats for biodiversity) interact. The conceptual model was based on four submodels that provided complementary representations of i) water governance, ii) agriculture, and iii) bird and iv) fish communities. The conceptual model highlights the strong relationships (i.e., causal chains, feedback loops, interactions, side effects, and trade-offs) among the multiple entities of this deltaic SES. It provides information about the region and describes deltas as a SES to support decision-making. One major feedback loop in the delta concerned relationships among regulations, rice fields, and use of marshes as a habitat for birds: strengthening pesticide regulations to improve water quality and bird habitats caused the area of rice cultivation to decrease which degraded habitat conditions for birds. Directly related to pesticide use, the most common trade-off is to decide whether water management should prioritize water quality or quantity in the delta’s Vaccarès lagoon, and opinion differs among stakeholders. The conceptual model could be used as a starting point to develop computer-based integrated assessment and modeling to explore dynamics of and resilience to climate change in the Ile de Camargue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".