Performance and competitiveness of red vs. green phenotypes of a cyanobacterium grown under artificial lake browning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it