Water Quality and Land Use Shape Bacterial Communities Across 621 Canadian Lakes
Bibliographic record
Abstract
Human activities such as agriculture and urban development are linked to water quality degradation. Canada represents a large and heterogeneous landscape of freshwater lakes, where variations in climate, geography and geology interact with land cover alteration to influence water quality differently across regions. In this study, we investigated the influence of water quality and land use on bacterial communities across 12 ecozones. At the pan-Canadian scale, total phosphorus (TP) was the most significant water quality variable influencing community structure, and the most pronounced shift was observed at 110 μg/L of TP, corresponding to the transition from eutrophic to hypereutrophic conditions. At the regional scale, water quality significantly explained bacterial community structure in all ecozones. In terms of land use effect, at the pan-Canadian scale, agriculture and, to a lesser extent, urbanisation were significant land use variables influencing community structure. Regionally, in ecozones characterised by extensive agriculture, this land cover variable was consistently significant in explaining community structure. Likewise, in extensively urbanised ecozones, urbanisation was consistently significant in explaining community structure. Overall, these results demonstrate that bacterial richness and community structure are influenced by water quality and shaped by agriculture and urban development in different ways.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".