Quantitating active photosystem II reaction center content from fluorescence induction transients
Bibliographic record
Abstract
Abstract Photosystem II (PSII) is a pigment‐protein complex that photochemically extracts electrons from water, generating the reductant that supports biological productivity in all biomes. Estimating the content of active PSII reaction centers in a liquid sample is a key input for estimating aquatic photosynthesis rates, as well as for analyzing phytoplankton stress responses. Established procedures for PSII content quantification based on oxygen evolution are slow, imprecise and require dense cell suspensions, and are thus inapplicable to many laboratory or field studies. A new approach uses baseline chlorophyll fluorescence emission divided by the effective absorbance cross section for PSII photochemistry, with both variables derivable from single turnover fluorescence induction protocols. This approach has not been widely tested and is potentially subject to variation in samples suffering progressive photoinactivation or induction of non‐photochemical quenching under variable light. We evaluated the validity of this approach for a marine picocyanobacteria, low and high light Prochlorococcus ecotypes, arctic and temperate prasinophyte green alga and two centric diatoms, generating 209 paired determinations from a range of growth and treatment conditions. We successfully calibrated the fluorescence derived estimator for PSII reaction center content, and demonstrate a modification that corrects for the short term influence of photoinactivation. The modified parameter shows little response to induction of non‐photochemical quenching. In doing so we show the potential and limitations of an estimator of active PSII reaction center content that is sufficiently robust to support rapid, time‐resolved autonomous measures of primary productivity from lakes and oceans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".