Bibliographic record
Abstract
In today's globalized world, humans and nature are inextricably linked.The coupled human and natural systems (CHANS) framework provides a lens with which to understand such complex interactions.One of the central components of the CHANS framework involves examining feedbacks among human and natural systems, which form when effects from one system on another system feed back to affect the first system.Despite developments in understanding feedbacks in single disciplines, interdisciplinary research on CHANS feedbacks to date is scant and often site-specific, a shortcoming that prevents complex coupled systems from being fully understood.The special feature "Exploring Feedbacks in Coupled Human and Natural Systems (CHANS)" makes strides to fill this critical gap.Here, as an introduction to the special feature, we provide an overview of CHANS feedbacks.In addition, we synthesize key CHANS feedbacks that emerged in the papers of this special feature across agricultural, forest, and urban landscapes.We also examine emerging themes explored across the papers, including multilevel feedbacks, time lags, and surprises as a result of feedbacks.We conclude with recommendations for future research that can build upon the foundation provided in the special feature.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".