Performance and competitiveness of red vs. green phenotypes of a cyanobacterium grown under artificial lake browning
Bibliographic record
Abstract
Increasing inputs of dissolved organic matter (DOM) to northern lakes is resulting in ‘lake browning.’ Lake browning profoundly affects phytoplankton community composition by modifying two important environmental drivers—light and nutrients. The impact of increased DOM on native isolates of red and green-pigmented cyanobacteria identified as Pseudanabaena, which emerged from a Dolichospermum bloom (Dickson Lake, Algonquin Provincial Park, Ontario, Canada) in 2015, were examined under controlled laboratory conditions. The genomes were sequenced to identify phylogenetic relatedness and physiological similarities, and the physical and chemical effects of increased DOM on cellular performance and competitiveness were assessed. Our study findings were that the isolated red and green phenotypes are two distinct species belonging to the genus Pseudanabaena; that both isolates remained physiologically unaffected when grown independently under defined DOM regimes; and that neither red nor green phenotype achieved a competitive advantage when grown together under defined DOM regimes. While photosynthetic pigment diversity among phytoplankton offers niche-differentiation opportunities, the results of this study illustrate the coexistence of two distinct photosynthetic pigment phenotypes under increasing DOM conditions.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".