Spatio-temporal dynamics of bacterial communities in the shoreline of Laurentian great Lake Erie and Lake St. Clair’s large freshwater ecosystems
Bibliographic record
Abstract
BACKGROUND: Long-term trends in freshwater bacterial community composition (BCC) and dynamics are not yet well characterized, particularly in large lake ecosystems. We addressed this gap by temporally (15 months) and spatially (6 sampling locations) characterizing BCC variation in lakes Erie and St. Clair; two connected ecosystems in the Laurentian Great Lakes. RESULTS: We found a spatial variation of the BCC between the two lakes and among the sampling locations (significant changes in the relative abundance of 16% of the identified OTUs at the sampling location level). We observed five distinct temporal clusters (UPGMA broad-scale temporal variation) corresponding to seasonal variation over the 15 months of sampling. Temporal variation among months was high, with significant variation in the relative abundance of 69% of the OTUs. We identified significant differences in taxonomic composition between summer months of 2016 and 2017, with a corresponding significant reduction in the diversity of BCC in summer 2017. CONCLUSIONS: As bacteria play a key role in biogeochemical cycling, and hence in healthy ecosystem function our study defines the scope for temporal and spatial variation in large lake ecosystems. Our data also show that freshwater BCC could serve as an effective proxy and monitoring tool to access large lake health.
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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 | 0.001 |
| 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) | 0.001 | 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".