Change in biomass of benthic and planktonic algae along a disturbance gradient for 24 Great Lakes coastal wetlands
Bibliographic record
Abstract
We quantified the chlorophyll a content of planktonic algae and benthic algae in periphyton on acrylic rods and in epiphyton growing on macrophytes in 24 coastal wetlands in all five Laurentian Great Lakes. Sites were selected to represent a wide range of environmental conditions ranging from nutrient-poor, clear-water marshes with abundant macrophytes to nutrient-enriched, turbid systems devoid of aquatic vegetation. Water quality and species and percent cover of submergent macrophytes were measured in each wetland. Principal components analysis (PCA) showed that total phosphorus, turbidity, and suspended solids, variables associated with human-induced degradation, were most strongly correlated with PC axis 1 (PC1), accounting for 69% of the total variation. The PC1 site score was significantly related to both periphyton and phytoplankton biomass, respectively accounting for 54 and 70% of the total variation in periphyton and phytoplankton data, whereas PC1 only accounted for 18% of the variation in epiphyton biomass. Periphytic and epiphytic biomass were negatively correlated with percent cover and species richness of submergent macrophytes, but phytoplankton biomass was not. We conclude that periphytic and planktonic chlorophyll a biomass are good indicators of human-induced water-quality degradation and recommend that both benthic and planktonic algal biomass should be routinely monitored as part of an effective wetland management program.
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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 | 0.000 |
| 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 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".