Telecoupling Toolbox: spatially explicit tools for studying telecoupled human and natural systems
Bibliographic record
Abstract
Telecoupling is a novel interdisciplinary umbrella concept that enables natural and social scientists to understand and generate information for managing how humans and nature can sustainably coexist worldwide.The telecoupling framework gains its distinction by enabling researchers to dive deeply into systemic complexities, even if systems are far away from each other.It is also ambitious in its aim to meet challenges unencumbered by disciplines.To understand the forces affecting sustainability across local to global scales, it is essential to build a comprehensive set of spatially explicit tools for describing and quantifying multiple reciprocal socioeconomic and environmental interactions over distances.We introduce the Telecoupling Toolbox, the first set of tools developed to map and identify the five major interrelated components of the telecoupling framework: systems, flows, agents, causes, and effects.The modular design of the toolbox allows the integration of existing tools and software to assess synergies and trade-offs associated with policies and other local to global interventions.We show applications of the toolbox by using two representative telecoupling case studies that address a variety of socioeconomic and environmental issues.The results suggest that the toolbox can systematically map and quantify multiple telecouplings under various contexts while providing users with an easy-to-use interface.It is our hope that the innovative, free, and open-source toolbox can provide a useful platform to address globally important issues, such as land use and land cover change, species invasion, migration, flows of ecosystem services, and trade of goods and products.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".