Changes in the distribution and abundance of Dreissena polymorpha within lakes through time. Hydrobiologia
Bibliographic record
Abstract
Dreissena polymorpha population densities and biomass were followed in three Belarusian lakes with different trophic status over a 12-year period subsequent to initial colonization. In all three lakes zebra mussel population densities did not change once they reached a maximum. Application of the Ramch-aran et al. [1992. Canadian Journal of Fisheries and Aquatic Sciences 49: 2611–2620] model for pre-dicting population dynamics of zebra mussels was accurate for two of the three lakes studied. Population density appears to depend on the time since initial colonization, relative abundance of substrate available for colonization, lake morphometry and trophic type. Zebra mussel distribution within lakes was highly patchy, but the degree of dispersion decreased over time after initial colonization, which may be a result of saturation of suitable substrates by zebra mussels as populations increase and reach carrying capacity. In lakes where submerged macrophytes are the dominant substrate for zebra mussel attachment, pop-ulations may be less stable than in lakes with a variety of substrates, which will have a more balanced age distribution, and be less impacted by year to year variation in recruitment. Dreissena polymorpha usually reach maximum population density 7–12 years after initial introduction. However, the timing of initial introduction is often very difficult to determine. Both European and North American data suggest that zebra mussels reach maximum density in about 2–3 years after populations are large enough to be detected.
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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 | 0.001 |
| 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".