The missing middle – Investigating a North American metalimnetic cyanobacteria layer
Bibliographic record
Abstract
While the majority of cyanobacteria research and bloom reports pertain to surficial events, research centred on subsurface cyanobacteria remains understudied. Metalimnetic cyanobacteria layers (MCL) are a subsurface phenomenon forming distinct depth stratum, often going unreported due to their inconspicuous nature, particularly in a North American context. Sunfish Lake (Ontario, Canada) represents a North American lake known for hosting an MCL. Here, we (1) reconstructed long-term cyanobacteria records to establish the changing risk of cyanobacteria blooms; and (2) investigated the spatial distribution of cyanobacteria and toxin-producing potential with real-time monitoring. The sediment record at Sunfish Lake revealed an unprecedented abundance of cyanobacteria in modern times (i.e., 1980s onwards), coinciding with increasingly warmer and wetter climatic conditions in the region. Real-time monitoring (2017) revealed an MCL and subsequent toxin analysis showed that peak toxin production (anabaenopeptin and microcystin) coincided with the MCL. Our findings provide (1) evidence for climate-driven shifts in cyanobacteria abundance and that even incremental alterations in climate signals over short temporal scales can push freshwater lakes towards cyanobacteria dominance; (2) importance of comprehensive monitoring to avoid “missing the middle” due to potential health risks at greater depths.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".