Predicting the number of ecologically harmful exotic species in an aquatic system
Bibliographic record
Abstract
ABSTRACT Most introduced species apparently have little impact on native biodiversity, but the proliferation of human vectors that transport species worldwide increases the probability of a region being affected by high‐impact invaders – i.e. those that cause severe declines in native species populations. Our study determined whether the number of high‐impact invaders can be predicted from the total number of invaders in an area, after controlling for species–area effects. These two variables are positively correlated in a set of 16 invaded freshwater and marine systems from around the world. The relationship is a simple linear function; there is no evidence of synergistic or antagonistic effects of invaders across systems. A similar relationship is found for introduced freshwater fishes across 149 regions. In both data sets, high‐impact invaders comprise approximately 10% of the total number of invaders. Although the mechanism driving this correlation is likely a sampling effect, it is not simply the proportional sampling of a constant number of repeat‐offenders; in most cases, an invader is not reported to have strong impacts on native species in the majority of regions it invades. These findings link vector activity and the negative impacts of introduced species on biodiversity, and thus justify management efforts to reduce invasion rates even where numerous invasions have already occurred.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".