Interactions between chemical and environmental factors and bacterial community composition in a Great Lakes watershed
Bibliographic record
Abstract
Urbanization and agricultural activity have degraded the water quality of the Great Lakes and their tributaries. These impacts include nutrient and fecal coliform loading and high sediment levels. In this presentation, we report on three years of continuous weekly monitoring of the Macatawa watershed in Ottawa and Allegan Counties, Michigan, a drowned river mouth entering Lake Michigan. Five lake locations and seven stream sites have been analyzed for microbiological content by community 16S rRNA sequencing, fecal indicator bacteria presence, and Escherichia coli whole‐genome sequencing. Along with these biological indicators, we profile chemical and physical parameters including dissolved oxygen, nutrients, temperature, and pH. We find continued high phosphorus, total suspended solids, and E. coli levels, exceeding total maximum daily load targets, or other benchmarks, for the watershed. Patterns in microbial communities show variation influenced by season and geographic location; for example, total E. coli increases in the stream tributaries in summer but decreases in the lake fed by those streams relative to winter. Furthermore, overall diversity of microbial communities increases in late fall and winter. These data provide a baseline for monitoring remediation efforts in the Macatawa Watershed. We suggest these will serve as a comparison for hypereutrophic watersheds around the nation and to aid decisions about remediations and public access to these recreational waters. Support or Funding Information NSF RUI 1616737
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".