CYANOBACTERIAL ACCLIMATION TO RAPIDLY FLUCTUATING LIGHT IS CONSTRAINED BY INORGANIC CARBON STATUS<sup>1</sup>
Bibliographic record
Abstract
Acclimation to rapidly fluctuating light, simulating shallow aquatic habitats, is altered depending on inorganic carbon (Ci) availability. Under steady light of 50 μmol photons·m−2·s−1, the growth rate of Synechococcus elongatus PCC7942 was similar in cells grown in high Ci (4 mM) and low Ci (0.02 mM), with induced carbon concentrating mechanisms compensating for low Ci. Growth under fluctuating light of a 1‐s period averaging 50 μmol photons·m−2·s−1 caused a drop in growth rate of 28%±6% in high Ci cells and 38%±8% in low Ci cells. In high Ci cells under fluctuating light, the PSI/PSII ratio increased, the PSII absorption cross‐section decreased, and the PSII turnover rate increased in a pattern similar to high‐light acclimation. In low Ci cells under fluctuating light, the PSI/PSII ratio decreased, the PSII absorption cross‐section decreased, and the PSII turnover remained slow. Electron transport rate was similar in high and low Ci cells but in both was lower under fluctuating than under steady light. After acclimation to a 1‐s period fluctuating light, electron transport rate decreased under steady or long‐period fluctuating light. We hypothesize that high Ci cells acclimated to exploit the bright phases of the fluctuating light, whereas low Ci cells enlarged their PSII pool to integrate the fluctuating light and dampen the variation of the electron flux into a rate‐restricted Ci pool. Light response curves measured under steady light, widely used to predict photosynthetic rates, do not properly predict photosynthetic rates achieved under fluctuating light, and exploitation of fluctuating light is altered by Ci status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".