Twenty-six years (1990–2015) of monitoring annual recruitment of the invasive zebra mussel (<i>Dreissena polymorpha</i>) in the Rideau River, a small river system in Eastern Ontario, Canada
Bibliographic record
Abstract
We monitored the recruitment of young-of-year zebra mussels (Dreissena polymorpha (Pallas, 1771)) each autumn at 13 locations within four river reaches along the length (100 km) of the Rideau River, starting in 1990, the year of its discovery in that river, until 2015. Sampling was conducted on bottom structures of locks or on seasonally exposed substrate during autumn drawdowns conducted by Rideau Canal staff. Twenty-six years of monitoring zebra mussels in that river revealed a distinct and persistent upstream–downstream pattern, with highest densities occurring in the two downstream reaches. A “lake effect” was observed at Long Reach, where veligers have ideal conditions for larval development. Highest densities occurred in the mid-1990s, comparable with those reported in the Laurentian Great Lakes during peak invasion (200 000 to 500 000+ mussels/m2). Although the most upstream reaches of the river had low recruitment rates and low densities initially (0.01 to 10 mussels/m2), annual recruitment progressively increased to higher values (10 to 1000+ mussels/m2) because more veligers from the Rideau Lakes and the river headwaters were produced and drifted into the system. This study is unique because it provides a thorough understanding of the 26 years of invasion history of the zebra mussel in a small river system.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".