Paleolimnological evidence reveals climate-related preeminence of cyanobacteria in a temperate meromictic lake
Bibliographic record
Abstract
Meromictic lakes provide a physically stable environment in which proxies for potentially harmful cyanobacteria are exceptionally well-preserved in the sediments. In Sunfish Lake, a meromictic lake that has recently become the focus of citizen concern due to the apparent rise in cyanobacteria blooms, we used a multi-proxy paleolimnological approach pairing novel spectral (i.e., VNIRS) and molecular (i.e., qPCR) assessment tools to explore long-term cyanobacteria trends. We hypothesized that climate change over the past 50 years altered the Sunfish Lake environment to favour cyanobacteria dominance, resulting in an increased incidence of bloom events. Spectral and genetic results aligned to reveal an unprecedented abundance of cyanobacteria in modern times and coincided with warmer and wetter climatic conditions in the region. Our findings offer evidence for climate-driven shifts in cyanobacteria abundance and suggest that a shift towards warmer and wetter conditions supports the rise of cyanobacteria in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".