Phytoplankton community composition in relation to water quality and water-body morphometry in urban lakes, reservoirs, and ponds
Bibliographic record
Abstract
Distinct differences in observed summer phytoplankton communities in relation to maximum depth suggest that constraints posed by water-body morphometry may modify the trophic control of phytoplankton-community composition and structure in urban water bodies. In deep urban sites (Z max [Formula: see text] 5 m), phytoplankton communities tended to be predictably related to trophic status (i.e., increases in trophy were associated with increased cyanobacterial dominance, a decreased proportion of Chrysophyceae-Synurophyceae and grazable-size algae, and a decrease in community richness), although exceptions existed, owing to factors such as human intervention, age of the water body, and flushing rates. In contrast, in shallow urban water bodies (Z max < 5 m), trophic status was a poor predictor of phytoplankton communities. Across meso- to hyper-eutrophic conditions, shallow urban sites were rarely dominated by cyanobacteria and, when they were, the species composition differed from nutrient-rich deep urban sites. The key requirement for cyanobacterial dominance in shallow urban sites appears to be sufficiently long water residence times, viz., greater than 8-14 days. Further study should describe how the relationship between water body residence time and species-generation time may limit the development of specific nuisance algal species, aiding in the management and rehabilitation of urban water bodies.
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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) | 0.001 | 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".