Grass carp in the Great Lakes region: establishment potential, expert perceptions, and re-evaluation of experimental evidence of ecological impact
Bibliographic record
Abstract
Intentional introductions of nonindigenous fishes are increasing globally. While benefits of these introductions are easily quantified, assessments to understand the negative impacts to ecosystems are often difficult, incomplete, or absent. Grass carp (Ctenopharyngodon idella) was originally introduced to the United States as a biocontrol agent, and recent observations of wild, diploid individuals in the Great Lakes basin have spurred interest in re-evaluating its ecological risk. Here, we evaluate the ecological impact of grass carp using expert opinion and a suite of the most up-to-date analytical tools and data (ploidy assessment, eDNA surveillance, species distribution models (SDMs), and meta-analysis). The perceived ecological impact of grass carp by fisheries experts was variable, ranging from unknown to very high. Wild-caught triploid and diploid individuals occurred in multiple Great Lakes waterways, and eDNA surveillance suggests that grass carp are abundant in a major tributary of Lake Michigan. SDMs predicted suitable grass carp climate occurs in all Great Lakes. Meta-analysis showed that grass carp introductions impact both water quality and biota. Novel findings based on updated ecological impact assessment tools indicate that iterative risk assessment of introduced fishes may be warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".