COMPARISON OF RESISTANCE TO LIGHT STRESS IN TOXIC AND NON‐TOXIC STRAINS OF <i>MICROCYSTIS AERUGINOSA</i> (CYANOPHYTA)<sup>1</sup>
Bibliographic record
Abstract
Blooms of Microcystis aeruginosa (Kützing) Kützing occur frequently in many freshwater ecosystems around the world, but the role of environmental factors in promoting the growth and determining the proportion of toxic and non-toxic strains still requires more investigation. In this study, four strains (toxic CPCC299 & FACHB905 and non-toxic CPCC632 & FACHB315) were exposed to high light (HL) condition, similar to light intensity found at the surface of a bloom, to evaluate their sensitivity to photoinhibition. We also estimated their capacity to recover from this HL stress. For all strains, our results showed an increased inhibition of the photosynthetic activity with HL treatment time. When comparing the extent of photoinhibition between strains, both toxic strains were more resistant to the treatment and recovered completely their photosynthetic capacity after 3 h, while non-toxic strains needed more time to recover. For toxic strains, the rETR under HL was higher compared to the rETR under low light (LL) control condition despite 50% photoinhibition. This suggests that the detrimental effect of high light (HL; up to 2 h) is outweighed by their higher photosynthetic potential. This conclusion did not stand for non-toxic strains, and indicates their preference for LL environment. We also demonstrated that a LL/HL cycle induced a 259% increase in cell yield for a toxic strain and a decrease by 22% for a non-toxic strain. This also indicates that toxic strains have higher tolerance to HL in a fluctuating light environment. Our data demonstrated that difference of sensitivity to HL between strains can modify the competitive outcome between toxic and non-toxic strains and may affect bloom toxicity.
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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".