Alien Invasions, Ecological Restoration in Cities and the Loss of Ecological Memory
Bibliographic record
Abstract
Abstract After a community or ecosystem is lost, it may leave behind an ecological memory. The site history, soil properties, spores, seeds, stem fragments, mycorrhizae, species, populations, and other remnants may influence the composition of the replacement community or ecosystem to varying degrees. The remnants may also hold the site to a trajectory that has implications for ecological restoration. This is true in urban situations in particular where repeated disturbance has masked the history of the site. The ecological memory remaining may be insufficient for a site to heal itself; restoration activities are required to direct the future of the site. Conversely, in light of climate change and other rapidly changing environments, the existing ecological memory may be poorly suited to the new conditions and restoration projects need to create new and perhaps novel ecosystems. The loss of ecological memory facilitates the establishment of foreign invasive species. These invasives may eventually create a new stability domain with its own ecological memory and degree of resilience. To be successful, invasive species control must address both internal within patch memory of invasives and external between patch memory. Further research is necessary to document and conserve ecological memory for ecological restoration in response to future ecosystem changes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".