Forecasting the Expansion of Zebra Mussels in the United States
Bibliographic record
Abstract
Because zebra mussels spread rapidly throughout the eastern United States in the late 1980s and early 1990s, their spread to the western United States has been expected. Overland dispersal into inland lakes and reservoirs, however, has occurred at a much slower rate than earlier spread via connected, navigable waterways. We forecasted the potential western spread of zebra mussels by predicting the overland movement of recreational boaters with a production-constrained gravity model. We also predicted the potential abundance of zebra mussels in two western reservoirs by comparing their water chemistry characteristics with those of water bodies with known abundances of zebra mussels. Most boats coming from waters infested with zebra mussels were taken to areas that already had zebra mussels, but a small proportion of such boats did travel west of the 100th meridian. If zebra mussels do establish in western U.S. water bodies, we predict that population densities could achieve similar levels to those in the Midwestern United States, where zebra mussels have caused considerable economic and ecological impacts. Our analyses suggest that the dispersal of zebra mussels to the western United States is an event of low probability but potentially high impact on native biodiversity and human infrastructure. Combining these results with economic analyses could help determine appropriate investment levels in prevention and control strategies.
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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 | 0.002 |
| 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 | 0.001 | 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".