Novel ecosystems in ecological restoration and rehabilitation: Innovative planning or lowering the bar?
Bibliographic record
Abstract
Stemming from a special symposium at the 2012 inaugural meeting of the Society for Ecological Restoration Australasia in Perth, Western Australia, this special issue editorial addresses novel ecosystems in ecological restoration and the inherent challenges of maintaining the highest standards of environmental stewardship and biological conservation in the face of increasing urbanization, agricultural expansion, and industrialization. Echoing others, we (the Guest Editors) view novel ecosystems as offering opportunities for conservation and restoration in the coming years and a pragmatic recognition that it may not always be possible, or desirable, to overcome adverse consequences of environmental degradation to reinstate historical systems. Being mindful of hubris and taking into account difficulties with identification, novel ecosystems may be viewed as a temporary or interim stage on the way towards the evolution of other future ecosystems able to supply a variety of ecosystem services, while attempting to maintain and enhance biodiversity, function and resilience. Here, a concise summary of contributions to the special issue and their significance to the field of restoration ecology is provided noting that authors were tasked to answer whether novel ecosystems are innovative planning or lowering the bar in ecological restoration. Core themes shared by the manuscripts are elucidated leading to guiding principles and, more importantly, an assessment of how and why restoration priorities are changing in the 21 st century.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".