Invasive species and their impacts on agri-ecosystems: issues and solutions for restoring ecosystem processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humans are the most invasive of vertebrates and they have taken many plants and animals with them to colonise new environments. This has been particularly so in Australasia, where Laurasian and domesticated taxa have collided with ancient Gondwanan ecosystems isolated since the Eocene Epoch. Many plants and animals that humans introduced benefited from their pre-adaptation to their new environments and some became invasive, damaging the biodiversity and agricultural value of the invaded ecosystems. The invasion of non-native organisms is accelerating with human population growth and globalisation. Expansion of trade has seen increases in purposeful and accidental introductions, and their negative impacts are regarded as second only to activities associated with human population growth. Here, the theoretical processes, economic and environmental costs of invasive alien species (i.e. weeds and vertebrate pests) are outlined. However, defining the problem is only one side of the coin. We review some theoretical underpinnings of invasive species science and management, and discuss hypotheses to explain successful biological invasions. We consider desired restoration states and outline a practical working framework for managing invasive plants and animals to restore, regenerate and revegetate invaded Australasian 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it