Applying ecological knowledge to the innovative design of sustainable agroecosystems
Bibliographic record
Abstract
Abstract The design of sustainable agroecosystems is crucial to meet contemporary environmental challenges such as biodiversity loss and global change. Ecological knowledge, although expected to be an important component of such an endeavour, is to date mainly used under a problem‐solving paradigm. Applying recent design theories, which highlight the differences between innovative design and problem solving, we assess the potential of using ecological knowledge in agroecosystem design in three contrasted French case studies representative of agricultural intensification world‐wide. In all cases, a design approach generated unexplored agroecosystem configurations and management alternatives. This analysis highlights that ecological science is critical for designing sustainable social‐ecological systems, because it orients the design process by identifying key ecological properties to maintain, while opening the range of management options stakeholders can explore. Synthesis and applications . Participatory design approaches of agroecosystems based on ecological knowledge might be key for planning and change: they allow a diversity of stakeholders to contribute to building solutions, thereby strengthening their sense of ownership and responsibility. Infrastructures in support of participatory design processes, set up in close relation to ecological research centres, have the potential to become new cornerstones of innovation for sustainable social‐ecological systems.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".