Climate Change Impacts on Water Temperatures in Urban Lakes: Implications for the Growth of Blue Green Algae in Fairy Lake
Bibliographic record
Abstract
Cyanobacteria, also known as blue-green algae, are photosynthetic bacteria that play a crucial role in aquatic ecosystems and are susceptible to changes in temperature. Hence, as global temperatures rise due to climate change, some Cyanobacterial species will thrive in warmer temperatures, which will result in increased blooms during the growing season. The MIKE-3 model is calibrated to existing (2022) conditions and is used to assess the impact of the RCP 4.5 scenario for the year 2050 in Fairy Lake (a shallow urban lake in Ontario). The simulations projected indicate that in 2050, in the central parts of Fairy Lake’s central basin, water temperatures will be above 20 °C for 2281 h compared to 2060 h in 2022. This situation indicates there will be a 10.7% increase in the duration of Cyanobacteria blooms in the central area of Fairy Lake. Similarly, in the northern area of Fairy Lake, the MIKE-3 model results indicate that surface temperature durations above 20 °C will increase from 1628 h to 2275 h for the year 2050, resulting in an additional 647 h of increased temperatures at the surface under RCP 4.5 scenario conditions. This situation indicates there will be a 39.7% increase in the duration of Cyanobacteria blooms in the northern portion of Fairy Lake. These modeling conditions indicate there will be significantly more habitat amenable to Cyanobacteria growth when surface water temperatures are above 20 °C, indicating substantial increases in the available growth time of Cyanobacteria due to climate change, all of which translate to major concerns caused by climate change.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".