Bibliographic record
Abstract
Abstract Islands, especially small oceanic islands, are highly sensitive to the introduction of alien biota. As islands have often been stopovers in the early development of international trade, there have been many opportunities for the introduction of alien organisms. As island food webs are simple, the addition of species can destabilise them (trophic interference) causing the collapse of normal trophic interactions and potentially the extirpation of whole ecosystems. In St Helena the native ecosystems were largely destroyed by events set in train after human discovery of the island, particularly the introduction of the goat. Ecosystem repair is challenging: total eradication of invasives can be problematic. If total eradication is impossible, classical biological control is an alternative. Gene drive biological control is an emerging technology that may be used in future. One consequence of wholesale ecosystem destruction is that many island species are ‘ultra‐rare’ (reduced to total populations of 10 individuals or less). In such cases, inbreeding may have caused fitness problems compounding extinction risk. In such cases, genetic rescue may be required, via the deliberate beneficial increase of the allele pool from other populations or even other species via adaptive introgression. Key Concepts Island ecosytems are vulnerable to introduced species. Terrestrial mamals do not disperse well and are absent from oceanic islands. Introduced species, especially mammalian herbivores and carnivores, destabilise existing trophic structure (trophic interference) and can lead to the collapse of ecosystems. Total eradication of invasives, or their biological control, can sometimes be possible to eliminate or mitigate the effect of invasives.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".