Predicting the invasion success of an introduced omnivore in a large, heterogeneous reservoir
Bibliographic record
Abstract
We demonstrate that invasion success, through the introduction and establishment stages, can generally be predicted based on biological characteristics of the organisms and physical aspects of the environment; however, predicting subsequent effects during integration is more challenging, especially for omnivorous fish species in large, heterogeneous systems. When gizzard shad (Dorosoma cepedianum) were incidentally introduced into Lake Powell, Utah–Arizona (2000), we predicted they would be successful invaders and would have food-web effects ranging from neutral to negative. As predicted, gizzard shad successfully established and dispersed throughout this large reservoir (300 km) within just 4 years, and their density was positively correlated with productivity. Also as predicted, gizzard shad exhibited fast growth rates, and striped bass (Morone saxatilis) predators were thus gape-limited, obtaining little gizzard shad forage. Contrary to our predictions, however, competition over zooplankton resources between gizzard shad and both threadfin shad (Dorosoma petenense) and juvenile striped bass appeared limited because of spatial segregation and diet preference. In sum, gizzard shad will continue to be successful invaders, but with limited effects on the established predator–prey cycle.
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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 | 0.001 |
| 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) | 0.000 | 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".