Microbial biodiversity of agricultural surface waters in Ontario, Canada
Bibliographic record
Abstract
Understanding the microbial diversity is the first step to monitor the microbial quality of agricultural surface waters. Here we report a study comparing the microbial composition and diversities in agricultural surface waters in Ontario, Canada, including the streams adjacent to dairy cattle production in eastern Ontario and the field runoffs from the manure-impacted experimental farm of south-western Ontario. We also studied the quality of the surface water in the western basin of Lake Erie during a harmful algal bloom (cHAB). In eastern Ontario, we found significantly lower ( P < 0.01) abundance of Proteobacteria and Burkholderiales in the streams adjacent to dairy production than the non-agriculture site. Bacteroidetes was more abundant ( P < 0.0001) in medium/high order streams and agriculture/urban mixed site. Seasonal peaks of Flavobacterium were observed in low order streams but the trend was not in medium/high order streams. In south-western Ontario, the dominant bacterial taxa in field runoffs was Pseudomonas, regardless of manure types. We also discovered that the elevated total dissolved nitrogen, total dissolved phosphorus and chlorophyll a associated with the cHAB zone in Lake Erie was further away rather from the agricultural sites. In conclusion, our studies identified unique microbiome patterns associated with agricultural surface waters in Ontario.
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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 | 0.001 | 0.003 |
| 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".