Synthetic ecosystems: an emerging opportunity for science and society?
Bibliographic record
Abstract
For millenia, humans have modified aspects of natural ecosystems to meet their social, economic and ecological needs. With advancing technology and global movement of species, modification has shifted to designing and creating new ecologies in cityscapes, building interiors, agricultural settings and more. We call intentional ecosystems that combine biodiversity and technology with little to no shared history, synthetic ecosystems. Fields, from microbial ecology to agroecology, build synthetic ecosystems under different names but share the same properties of: Being human‐designed, assembled and controlled, having novel components and/or interactions, and creating systems distinctly different from what came before at a site. Creating synthetic ecosystems represents a design challenge, but also an opportunity for real‐world impact – which we illustrate with a biodiverse, indoor synthetic ecosystem for food production. Overall, synthetic ecosystems may advance socioecological goals in six ways. They can: 1) replace ecological deadzones with living systems (e.g. building green roofs), 2) enhance existing ecosystem processes (e.g. boosting agricultural yields), 3) create new ecosystem functions (e.g. bioelectricity), 4) establish new ecosystem controls (e.g. biological control), 5) foster knowledge synthesis (e.g. testing ecological theory) and 6) reshape human‐nature relationships (e.g. improve wellbeing). To realize these potentials, future work must more fully evaluate whereandwhen synthetic ecosystems are appropriate to build, whatarchitectures and aspects of diversity (biological and technological) make them most functional and how knowledge from across cultures and eras can be integrated in solutions.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".