Soft sediment as a constraint on the spread of the zebra mussel in western Lake Erie: Processes and impacts
Bibliographic record
Abstract
The zebra mussel (Dreissena), inadvertently introduced to the Great Lakes in 1986, has since expanded to cover most of the shallow-water, hard substrates in Lakes Erie and Ontario. Colony densities exceed 300,000 per m2 in some bedrock areas of western Lake Erie. The objective of this study was to investigate the spread of zebra mussel onto soft sediment areas of the western basin of Lake Erie and to identify natural controls on the large-scale colonization of such sediments. Combined side scan sonar, underwater video imagery, and direct diver observation showed three modes of viable zebra mussel colonies in soft substrates: 1) attachment to zebra mussel shell debris deposited in linear troughs (stripes); 2) attachment to shells built up over hard substrates intermittently covered by soft sediments (footballs); and 3) as isolated clumps (druses) attached to dropstones, unionid clams, or their shells. Their spatial distribution suggests that zebra mussel expansion onto soft sediments is supplied primarily by nearby hard substrate areas. Zebra mussel populations living on soft sediment have the normal size distribution as those found on hard surfaces, often with a bimodal or trimodal character, representing cohorts of different ages. At the sites studied, there was a large proportion of dead shells, suggesting colonization over an extended period, as well as a relatively high mortality rate due to burial by periodic catastrophic sedimentation after storms. This vulnerability and the need to be near to source areas make it unlikely that the zebra mussel (D. polymorpha) will continue to expand into areas of soft sediment remote from hard substrate areas. The impact of the zebra mussel colonies on important textural properties of the substrate was not dramatic, but median grain diameter was significantly finer, organic carbon significantly higher, and sediment consistency more clayey below zebra mussel mats. Metal content was generally higher in the samples below zebra mussel mats, but the differences were statistically significant only in the case of iron and maganese. However, the sediment concentrations of metals at all sites were much greater than those at the remote Mid-basin site that was barren of zebra mussels, suggesting an overall metal enrichment near zebra mussel colonies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".