Invasive Species Research to Meet the Needs of Resource Management and Planning
Bibliographic record
Abstract
As zebra mussels (Dreissena polymorpha) continue to spread among inland lakes of the United States and Canada, there is growing interest from professionals, citizens, and other stakeholders to know which lakes are likely to be colonized by zebra mussels. Thus, we developed a classification of lake suitability for zebra mussels on the basis of measured or estimated concentrations of dissolved calcium in lake water and applied the classification to >11,500 lakes in Wisconsin and the Upper Peninsula of Michigan. The majority of lakes (58%) were classified as unsuitable (<10 mg/L Ca) for survival and reproduction of zebra mussels, 27% were identified as suitable (≥21 mg/L Ca), and 15% were classified as borderline suitable (≥10 and <21 mg/L Ca). Of the 77 inland lakes with confirmed zebra mussel records for which data on dissolved calcium were available, our method classified 74 as suitable and 3 as borderline suitable. To communicate this lake-specific suitability information and to help prioritize regional efforts to monitor and prevent the expansion of zebra mussels and other invasive species, we developed a web-based interface (available from http://www.aissmartprevention.wisc.edu/). Although we are still uncertain of how access to suitability information ultimately affects decision making, we believe this is a useful case study of building communication channels among researchers, practitioners, and the public.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".