Microbial communities change along the 300 km length of the Grand River for extreme high- and low-flow regimes
Bibliographic record
Abstract
The Grand River watershed is the largest catchment in southern Ontario. The river's northern and southern sections are influenced by agriculture, whereas central regions receive wastewater effluent and urban runoff. To characterize in-river microbial communities, as they relate to spatial and environmental factors, we conducted two same-day sampling events along the entire 300 km length of the river, representing contrasting flow seasons (high flow spring melt and low flow end of summer). Through high-throughput sequencing of 16S rRNA genes, we assessed the relationship between river microbiota and spatial and physicochemical variables. Flow season had a greater impact on communities than spatial or diel effects and profiles diverged with distance between sites under both flow conditions, but low-flow profiles exhibited higher beta diversity. High-flow profiles showed greater species richness and increased presence of soil and sediment taxa, which may relate to increased input from terrestrial sources. Total suspended solids, dissolved inorganic carbon, and distance from headwaters significantly explained microbial community variation during the low-flow event, whereas conductivity, sulfate, and nitrite were significant explanatory factors for spring melt. This study establishes a baseline for the Grand River's microbial community, serving as a foundation for modeling the microbiology of anthropogenically impacted freshwater systems affected by lotic processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".