The influence of macrophyte beds on plankton communities and their export from fluvial lakes in the St Lawrence River
Bibliographic record
Abstract
1. To determine the influence of macrophyte beds on plankton abundance within fluvial lakes of the St Lawrence River, planktonic components (macrozooplankton, heterotrophic bacteria, and phytoplankton as chlorophyll‐ a [Chl‐ a ]) were sampled in Lake St Francis and Lake St Pierre during summer 1998. We tested the hypothesis that the abundance of planktonic components was higher within macrophyte beds in comparison to the more rapidly flushed open water areas of the fluvial lakes. 2. The large cross channel variation in zooplankton biomass was indeed correlated with the presence of dense beds of submerged macrophytes. Total macrozooplankton biomass was nine‐fold greater within the beds (mean=180 μg L−1 dry mass) than in either the open water or areas with only sparse vegetation (mean=20 μg L−1 dry mass). 3. Chl‐ a and heterotrophic bacterial abundance were also higher in the beds, but only slightly so. There was no difference in total phosphorus or dissolved organic carbon concentrations between areas of dense vegetation, sparse vegetation or open water. 4. Macrophyte beds on the margins of the fluvial lakes allow the development of high planktonic abundance relative to the fast flowing central channel. Macrozooplankton biomass was much higher at the outflows of the lakes (∼50 μg L−1 dry mass) in comparison to the inflows (<20 μg L−1 dry mass). The increase is due to the transfer of organisms from submerged macrophyte beds into the central channel in the downstream quarter of the two lakes where the marginal littoral waters enter central channel waters. 5. Along rivers, shallow fluvial lakes appear to act as sources of plankton which is exported downstream during years of extensive littoral macrophyte development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".