The Control of Biological Invasions in the World's Oceans
Bibliographic record
Abstract
Abstract: The introduction of alien, or nonindigenous, animals and plants has been identified by scientists and policy makers as a major threat to biodiversity in marine ecosystems. Although government agencies have struggled to control alien species on land and freshwater for decades with mixed success, the control of alien marine species is in its infancy. Prevention of introduction and establishment must be the first priority, but many populations of alien marine species are already well established worldwide. National and international policies leave loopholes for additional invasions to occur and provide only general guidance on how to control alien species once they are established. To address this issue, a multinational group of 25 scientists and attorneys convened in 1998 to examine options for controlling established populations of alien marine species. The discussions resulted in a framework for control of alien marine species to provide decision‐making guidance to policymakers, managers, scientists, and other stakeholders. The framework consists of seven basic steps: (1) establish the nature and magnitude of the problem, (2) set objectives, (3) consider the full range of alternatives, (4) determine risk, (5) reduce risk, (6) assess benefits versus risks, and ( 7) monitor the situation. This framework can provide guidance for control efforts under the existing patchwork of national laws and can help provide a foundation for international cooperation.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".