Regional differences in rates and patterns of North American inland lake invasions by zebra mussels (<i>Dreissena polymorpha</i>)
Bibliographic record
Abstract
Zebra mussels (Dreissena polymorpha) have spread rapidly in North America by dispersal within connected bodies of waters. This study provides the first systematic evaluation of rates of zebra mussel dispersal to inland lakes separated from source populations by functional dispersal barriers. Plankton samples were examined for this exotic species from 140 lakes during a 3-year period (1995-1997). Infestations were detected in 19% of lakes surveyed: seven of 28 Indiana lakes (25%), 15 of 49 Michigan lakes (31%), but only five of 63 Wisconsin-Illinois lakes (8%). Annual rates of infestation varied from 0 to 12%·year-1 among the three regions. Wisconsin-Illinois lake infestations were only detected in 1995 and 1996, whereas new Indiana and Michigan infestations were detected in all three years. Lakes with surface areas less than 100 ha had lower infestation rates than larger lakes. Incidental sightings of inland lake colonization within the study region qualitatively supported observed regional differences in rates and spatial patterns of colonization. These results demonstrate that the spread of zebra mussels into inland lakes is not occurring as rapidly as through connected waterways, and rates of inland lake colonization vary according to regional conditions and lake size.
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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 | 0.000 | 0.000 |
| 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".