The diversity and community composition of aquatic macrophytes in relation to physical and chemical environmental variables in the Rideau River, Ontario.
Bibliographic record
Abstract
Like many rivers around the world, the Rideau River is under pressure from a number of human induced activities resulting in a loss of species and habitat. In order to prioritise conservation efforts, it is necessary to understand what type of habitats will support the widest range of species. This study examines which physical and chemical factors exert the strongest influence on the diversity and community composition of aquatic macrophytes in the Rideau River. Macrophyte species were surveyed at 33 sites on the Rideau River, Ontario, in six 1 m2 quadrats aligned in a belt transect perpendicular to shore along a depth gradient of 0.5 m to 2.0 m. Regression analysis showed species richness and Shannon diversity were significantly related to water velocity, transect length, slope, and organic content. Multiple regression provided a model whereby 70% of species richness was explained by organic content, transect length, water velocity and chlorophyll a, and 77% of Shannon diversity was explained by organic content and water velocity. Mantel tests showed only chlorophyll a was weakly correlated with species composition. Canonical correlation analysis showed floating and floating-leaved species to favour habitats with low water velocity. No other significant patterns were found. It appears that while species diversity can be predicted from physical environmental variables, species composition cannot.
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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 | 0.000 |
| 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".