Bibliographic record
Abstract
Abstract Growing attention to novel and designed ecosystems, and the confusion that follows from the overlap of these distinct ecosystem approaches, risks a loss of focus on ecological values at the core of restoration ecology. Novel ecosystems originate in ecosystems that are transformed beyond which the practical efforts of conventional restoration are feasible. They are also self‐sustaining in the sense that they take time to form, and do not typically receive regular management. In this respect, they arise differently than designed ecosystems, which are assembled with specific goals in mind and are often heavily managed. Designed (or engineered) ecosystems comprise a variety of ecological approaches including reclamation (return a degraded ecosystem to productive capacity), green infrastructure, and agroecological systems. There are three elements that distinguish novel and designed ecosystems. Designed ecosystems typically require intensive intervention to create them, and ongoing management to sustain them; novel ecosystems do not. Second, the human intentions behind designed and novel ecosystems are usually different. Designed ecosystems exist in the service of human interests, including specific services (e.g. filtration, cooling, nature appreciation), aesthetics, and shifting value commitments toward green infrastructure; novel ecosystems arise typically through inadvertent human activity. Third, designed and novel ecosystems have different developmental pathways. Historical ecosystems are the starting point for restored, hybrid, and novel ecosystems; designed ecosystems are intentionally created. Designed ecosystems stand apart as providing a new origin for ecosystems of the future, including those that become novel ecosystems.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".