Geological and evolutionary underpinnings for the success of Ponto-Caspian species invasions in the Baltic Sea and North American Great Lakes
Bibliographic record
Abstract
Between 1985 and 2000, ~70% of new species that invaded the North American Great Lakes were endemic to the Ponto-Caspian (Caspian, Azov, and Black seas) basins of eastern Europe. Sixteen Ponto-Caspian species were also established in the Baltic Sea as of 2000. Many Ponto-Caspian endemic species are characterized by wide environmental tolerances and high phenotypic variability. Ponto-Caspian fauna evolved over millions of years in a series of large lakes and seas with widely varying salinities and water levels and alternating periods of isolation and open connections between the Caspian Sea and Black Sea depressions and between these basins and the Mediterranean Basin and the World Ocean. These conditions probably resulted in selection of Ponto-Caspian endemic species for the broad environmental tolerances and euryhalinity many exhibit. Both the Baltic Sea and the Great Lakes are geologically young and present much lower levels of endemism. The high tolerance of Ponto-Caspian fauna to varying environmental conditions, their ability to survive exposure to a range of salinities, and the similarity in environmental conditions available in the Baltic Sea and Great Lakes probably contribute to the invasion success of these species. Human activities have dramatically increased the opportunities for transport and introduction and have played a catalytic role.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".