Forecasting the distribution of the invasive round goby (Neogobius melanostomus) in Wisconsin tributaries to Lake Michigan
Bibliographic record
Abstract
The Laurentian Great Lakes host more than 180 non-native species, including several that have resulted in major economic and ecological effects. This list includes the round goby ( Neogobius melanostomus ), an aggressive, benthic Ponto-Caspian fish that has established large populations in coastal Great Lakes habitats. Here, we document the inland dispersal of round gobies into Wisconsin tributaries of Lake Michigan. Round gobies were detected in 26 of 73 streams (36%) and found >10 km upstream of Lake Michigan in nine watersheds. Round goby presence–absence was modeled using landscape-scale data from these invaded streams. We forecasted the future spread of round goby within Wisconsin’s Lake Michigan basin using our best model (80% accuracy), which included watershed area, stream gradient, and watershed slope as predictors. Round gobies were predicted to invade 1369 km of stream habitat up to the first stream barrier, and 8878 km of stream was identified as suitable looking beyond barriers at the broader Lake Michigan watershed (Wisconsin only). Our results depict the Great Lakes as a springboard for invasive species to disperse into inland ecosystems and, because round gobies are not usually reported in small streams in their native range, emphasize the utility of data from invaded regions when forecasting invasive species distributions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".