Geographical patterns in range extension of Ponto-Caspian macroinvertebrate species in Europe
Bibliographic record
Abstract
Range extensions of aquatic Ponto-Caspian macroinvertebrate species in Europe have mainly been facilitated by the interconnection of river basins through man-made canals and intentional introductions. Three inland migration corridors can be distinguished: (i) a northern corridor: Volga [Formula: see text] Lake Beloye [Formula: see text] Lake Onega [Formula: see text] Lake Ladoga [Formula: see text] Neva [Formula: see text] Baltic Sea, (ii) a central corridor connecting the rivers Dnieper [Formula: see text] Vistula [Formula: see text] Oder [Formula: see text] Elbe [Formula: see text] Rhine, and (iii) a southern corridor connecting the Danube and Rhine rivers. Important trade harbours in Europe were connected via these corridors allowing further range extensions of macroinvertebrate species attached to a vessel's hull or in ballast water. The central corridor was the main migration route before 1992, after which the southern corridor became the most important migration route for the range expansions to the west because of the reopening of the Main-Danube Canal, connecting the Rhine and Danube basins. Especially the water level maintenance in the upper part of the canal, with water supply from the Danube basin, facilitated migration of mobile animals (e.g., crustaceans) from the Danube basin towards the Rhine basin; however, contribution of other transport mechanisms (e.g., shipping) is expected in the near future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".