Are fluorometric, taxonomic, and functional indicators of phytoplankton community structure linked to environmental typology of urban ponds and lakes?
Bibliographic record
Abstract
Phytoplankton bioindicators were used to assess environmental conditions in urban aquatic systems in a large Canadian city. Sampling was conducted during summers 2010 and 2011 in 20 urban waterbodies on the Island of Montreal (Quebec, Canada). We evaluated 4 indicators: (1) fluorometric estimates of the chlorophyll concentration of the total phytoplankton and of 4 spectral groups, (2) species richness, and (3) biovolumes of taxonomic and (4) functional groups of the microphytoplankton. We assessed how these indicators changed among types of urban waterbodies and determined the limnological features and/or management practices driving their spatial variation. Principal component analysis captured 48% of total environmental heterogeneity, and K-means clustering analyses defined 5 relevant types of waterbodies. Overall, 96 microphytoplankton species (γ diversity) were recorded, and species richness (α diversity) per waterbody varied from 1 to 27. Chlorophyll concentration of the total phytoplankton and the green algae spectral group, as well as the total biovolume of microphytoplankton, dinophytes, and of 2 functional groups (large flagellates, large colonies of green algae, and cyanobacteria) differed among waterbody types. Phytoplankton indicators based on in situ fluorometry and microphytoplankton biovolume of functional and taxonomic groups were fairly coherent and showed potential for monitoring. Implications of our findings are discussed in light of guiding future management practices to sustain biodiversity, ecological integrity, and water quality of urban waterbodies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".