Different physiological responses of cyanobacteria to ultraviolet‐B radiation under iron‐replete and iron‐deficient conditions: Implications for underestimating the negative effects of <scp>UV</scp>‐B radiation
Bibliographic record
Abstract
Iron deficiency has been considered one of the main limiting factors of phytoplankton productivity in some aquatic systems including oceans and lakes. Concomitantly, solar ultraviolet-B radiation has been shown to have both deleterious and positive impacts on phytoplankton productivity. However, how iron-deficient cyanobacteria respond to UV-B radiation has been largely overlooked in aquatic systems. In this study, physiological responses of four cyanobacterial strains (Microcystis and Synechococcus), which are widely distributed in freshwater or marine systems, were investigated under different UV-B irradiances and iron conditions. The growth, photosynthetic pigment composition, photosynthetic activity, and nonphotochemical quenching of the different cyanobacterial strains were drastically altered by enhanced UV-B radiation under iron-deficient conditions, but were less affected under iron-replete conditions. Intracellular reactive oxygen species (ROS) and iron content increased and decreased, respectively, with increased UV-B radiation under iron-deficient conditions for both Microcystis aeruginosa FACHB 912 and Synechococcus sp. WH8102. On the contrary, intracellular ROS and iron content of these two strains remained constant and increased, respectively, with increased UV-B radiation under iron-replete conditions. These results indicate that iron-deficient cyanobacteria are more susceptible to enhanced UV-B radiation. Therefore, UV-B radiation probably plays an important role in influencing primary productivity in iron-deficient aquatic systems, suggesting that its effects on the phytoplankton productivity may be underestimated in iron-deficient regions around the world.
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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".