Physical Circulation in the Coastal Zone of a Large Lake Controls the Benthic Biological Distribution
Bibliographic record
Abstract
Abstract Gradients of conductivity and major ions in the coastal zone of the eastern Georgian Bay of Lake Huron appear to limit the spatial distribution of invasive dreissenid mussels. Rivers flowing into Georgian Bay from the Canadian Shield have relatively low conductivity compared to the main body of Lake Huron, which creates a gradient of solutes near the river mouths. Field observations show a strong positive correlation between conductivity and calcium concentration. Thus, we use conductivity to infer the calcium concentrations required for the successful growth of dreissenid mussels. Most dreissenid mussels were observed in regions where specific conductivities were greater than 140 μS/cm. Field observations were used to examine how the calcium poor river water mixes within the coastal zone, resulting in solute gradients that determine mussel distribution. When river flows are low in late summer, there is only a weak solute gradient across the coastal zone, implying an intrusion of open bay waters into the shallow embayments, that favor the growth of dreissenid mussels. In contrast, during spring when river flows are as much as 10 times higher, there is a strong solute gradient that extends further into the lake, and the low calcium appears to limit the growth of dreissenid mussels. Thus, the seasonal character of solute gradients helps describe the spatial distribution of dreissenid mussels and explains the localized absence of a species that is otherwise prevalent in much of the Laurentian Great Lakes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".