Bibliographic record
Abstract
Introduction Densities of invasive, introduced plants are generally higher in the exotic habitat than in their native habitats. Possible reasons for this have been discussed in Chapter 3 and include a lack of specialist herbivores and new competitive interactions with other plant species in the exotic habitat. If the success of invasive weeds is due to a lack of specialized herbivores and diseases, the introduction of natural enemies from the native habitat should redress the problem. This rationale, however, produces an ethical dilemma. Should more foreign species be introduced to adjust the balance between native and introduced plant species? A review of biological control in Canada showed that on average five to seven species of natural enemies were introduced for every exotic weed for which biological control was attempted. Of these, only 10% had any impact on host density. This ratio of introduced agents to targets is approximately 2.5 to 1 in other studies (McFadyen 2000). However, some biological control programs have involved a very large number of introductions of natural enemies. For example, over 20 species of natural enemies have been introduced in largely unsuccessful attempts to control Lantana (Broughton 2000) and 50 species of natural enemies were introduced early in the biological control programme against Opuntia cactus in Australia (Mann 1970). The practice of biological control increases the number of introduced species, and in this way has the potential to increase the ratio of exotic to native species.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".