Cyanobacterial blooms in a warming climate: paleolimnological assessments of three boreal shield lakes in central Ontario, Canada
Bibliographic record
Abstract
Paleolimnological techniques were used to assess long-term water quality changes in three cyanobacterial bloom-impacted lakes in Algoma, Ontario, Canada. Since the 2000s, frequent cyanobacterial blooms have been reported in these lakes despite stable nutrient levels, allowing investigation of recent climate warming as a possible environmental driver of the blooms. While diatom and chironomid community changes varied among the lakes, accelerated warming (∼1990) led to near-synchronous shifts in paleolimnological indicators, including increased sedimentary chlorophyll a, planktonic diatoms ( Discostella stelligera, elongate taxa), and chrysophyte scales. Diatom-inferred total phosphorus and chironomid-inferred hypolimnetic oxygen models indicate oligo-mesotrophic conditions prior to ∼1950, with slightly higher late-summer oxygen concentrations in Desbarats and Bright lakes. Our results suggest a shift towards enhanced thermal stability due to regional warming, supported by instrumental records documenting rising air temperature, reduced wind speed, and a longer ice-free period observed in the last half century. Our paleolimnological inferences indicate that climate-driven changes in fundamental lake physicochemical properties and internal nutrient loading may be contributing to, or even triggering, recent cyanobacterial blooms in these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".