A Call for Bio-Inspired Technologies: Promises and Challenges for Ecosystem Service Replacement
Bibliographic record
Abstract
Ecosystem services are crucial for animals, plants, the planet, and human well-being. Decreasing biodiversity and environmental destruction of ecosystems will have severe consequences. Designing technologies that could support, enhance, or even replace ecosystem services is a complex task that the Manufactured Ecosystems Project team considers to be only achievable with transdisciplinarity, as it unlocks new directions for designing research and development systems. One of these directions in the project is bio-inspiration, learning from natural systems as the foundation for manufacturing ecosystem services. Using soil formation as a case study, text-mining of existing scientific literature reveals a critical gap: fewer than 1% of studies in biomimetics address soil formation technological replacement, despite the rapid global decline in natural soil formation processes. The team sketches scenarios of ecosystem collapse, identifying how bio-inspired solutions for equitable and sustainable innovation can contribute to climate adaptation. The short communication opens the discussion for collaboration and aims to initiate future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".