Use of diatom algae as biological indicators for assessing and monitoring water quality of the rivers in the Greater Toronto Area, Canada
Bibliographic record
Abstract
Diatom and environmental data collected from the rivers in the Greater Toronto Area (GTA) were analyzed using various multivariate ordination and weighted averaging methods. A regional calibration set was based on the 280 samples collected from 42 sites of the Humber River on a monthly basis over a one-year period. Canonical Correspondence Analysis (CCA) showed that the first two axes were correlated with specific conductivity and temperature, suggesting that both spatial and temporal patterns were important. Specific conductivity was the most important variable and a diatom inference model was developed for this variable using the simple weighted average method with inverse de-shrinking. Subsequently, diatoms from five selected sites each belonging to one of five different land use categories and sampled over a 12-month period, were analyzed for temporal variations. Each land use site supported different diatom species, some of which further showed different seasonal changes. More polluted sites exhibited more distinct seasonal patterns, while less polluted sites showed less pronounced seasonal variability. For sites where seasonal variation was pronounced, distinct differentiation was found between winter and spring samples and summer and fall samples. Therefore it can be recommended to sample more than once per year, or to develop different diatom indices depending on the season. Despite the high seasonal variation in some sites, differences in overall diatom assemblages between more polluted and less polluted sites remained distinct, indicating that diatoms can be used as a powerful biomonitoring tool. The influence of substratum type on diatom composition and abundances was also tested by comparing pairs of rock and sand samples collected from 73 monitoring stations along six watersheds of the GTA. Results show that rocks and sand samples support similar diatom assemblages, with comparable species richness and diversity as well as growth forms. Correspondence Analysis (CA) did not show any separation between diatom assemblages from the two substrata, and CCA showed that the two assemblages not only have similar patterns in diatom distribution but are also controlled by the same environmental variables, suggesting that in river stretches where a preferred substratum is not present, the second choice substratum can be used.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".