Comparison of Cyanobacteria Phenotypes with Distinctive Photosynthetic Pigment Compositions to Simulated Lake Browning
Bibliographic record
Abstract
Browning of inland waters has been noted over large parts of the Northern hemisphere and is a phenomenon with both ecological and societal consequences. The increase in water color is generally ascribed to increasing concentrations of dissolved organic matter (cDOM) of terrestrial origin. Changes in water color will have profound effects on the phytoplankton composition in freshwater systems. Here, I examined the effect of changes in water color associated with coloured DOM (cDOM) on red and green phenotypes of the cyanobacterium, Pseudanabaena, which emerged to surface blooms in Dickson Lake (Algonquin Provincial Park, Ontario) in the summer of 2014. Results presented here indicated that: (A) the increased level of cDOM had little effect on the growth and photosynthetic activity of either phenotype when grown independently, suggesting that lake browning was a benign, rather than selective, ecological driver; and (B) neither phenotype achieved a competitive advantage when the two phenotypes were grown together under defined cDOM regimes suggesting coexistence of both phenotypes. These findings temper the ideas that, with climate change, only specific bloom forming cyanobacteria will prevail, as both phenotypes of Pseudanabaena were present and showed the ability to co-exist. Cyanobacteria of this genera are likely to thrive under warmer and browner conditions due to their photosynthetic pigment composition that allows them to capture light at a variety of wavelengths.
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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) | 0.001 | 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".