Ecosystem-level regulation of boreal lake phytoplankton by ultraviolet radiationThis paper is part of the series “Forty Years of Aquatic Research at the Experimental Lakes Area”.
Bibliographic record
Abstract
Unique and interactive effects of ultraviolet radiation (UVR), temperature, and water column mixing on phytoplankton abundance and community composition were quantified using regression and multivariate analysis for lakes of differing transparencies and UVR exposure regimes located at the Experimental Lakes Area in Canada. Abundance of planktonic diatoms and chrysophytes (as fucoxanthin) and total algae (as chlorophylls) were negatively correlated with UVB exposure (R2= 0.57 and R2= 0.64, respectively) in slightly stained lakes. In contrast, concentrations of both filamentous Cyanobacteria and dinoflagellates were positively correlated with UVB levels in eutrophic and humic lakes (dissolved organic carbon >9 mg·L–1) (R2= 0.23 and R2= 0.27), whereas all algal groups were uncorrelated with UVB in oligotrophic ecosystems. Although univariate analyses suggested that surface water temperature explained more variation in algal abundance than UVB, principal components analyses revealed that the two factors often covaried and could not be statistically disentangled. Instead, it appears that strong UVB effects on lake algal communities occur commonly when optical and thermal properties interact to maximize exposure to high irradiances. Unexpectedly, UVR could either suppress or stimulate surface algal growth depending on the precise combination of lake parameters and algal community composition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".