Aqueous Geochemical Controls on the Sestonic Microbial Community in Lakes Michigan and Superior
Bibliographic record
Abstract
Despite being the largest freshwater lake system in the world, relatively little is known about the sestonic microbial community structure in the Laurentian Great Lakes. The goal of this research was to better understand this ecosystem using high-throughput sequencing of microbial communities as a function of water depth at six locations in the westernmost Great Lakes of Superior and Michigan. The water column was characterized by gradients in temperature, dissolved oxygen (DO), pH, and other physicochemical parameters with depth. Mean nitrate concentrations were 32 μmol/L, with only slight variation within and between the lakes, and with depth. Mean available phosphorus was 0.07 μmol/L, resulting in relatively large N:P ratios (97:1) indicative of P limitation. Abundances of the phyla Actinobacteria, Bacteroidetes, Cyanobacteria, Thaumarchaeota, and Verrucomicrobia differed significantly among the Lakes. Candidatus Nitrosopumilus was present in greater abundance in Lake Superior compared to Lake Michigan, suggesting the importance of ammonia-oxidating archaea in water column N cycling in Lake Superior. The Shannon diversity index was negatively correlated with pH, temperature, and salinity, and positively correlated with DO, latitude, and N2 saturation. Results of this study suggest that DO, pH, temperature, and salinity were major drivers shaping the community composition in the Great Lakes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".