Generalist invasion in a complex lake food web
Bibliographic record
Abstract
Abstract Invasive species constitute a threat not only to native populations but also to the structure and functioning of entire food webs. Despite being considered as a global problem, only a small number of studies have quantitatively predicted the food web‐level consequences of invasions. Here, we use an allometric trophic network model parameterized using empirical data on species body masses and feeding interactions to predict the effects of a possible invasion of Amur sleeper ( Perccottus glenii ), on a well‐studied lake ecosystem. We show that the modeled establishment of Amur sleeper decreased the biomasses of top predator fishes by about 10%–19%. These reductions were largely explained by increased larval competition for food and Amur sleeper predation on fish larvae. In contrast, biomasses of less valued fish of lower trophic positions increased by about 0.4%–9% owing to reduced predation pressure by top piscivores. The predicted impact of Amur sleeper establishment on the biomasses of native fish species vastly exceeded the impacts of current‐day fishing pressures.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".