Direct estimation of functional PSII reaction center concentration and PSII electron flux on a volume basis: a new approach to the analysis of Fast Repetition Rate fluorometry (FRRf) data
Bibliographic record
Abstract
Phytoplankton primary productivity is most commonly measured by 14C assimilation although less direct methods, such as O2 exchange, have also been employed. These methods are invasive, requiring bottle incubation for up to 24 h. As an alternative, Fast Repetition Rate fluorometry (FRRf) has been used, on wide temporal and spatial scales within aquatic systems, to estimate photosystem II (PSII) electron flux per unit volume (JVPSII), which generally correlates well with photosynthetic O2 evolution. A major limitation of using FRRf arises from the need to employ an independent method to determine the concentration of functional photosystem II reaction centers ([RCII]); a requirement that has prevented FRR fluorometers being used, as stand‐alone instruments, for the estimation of electron transport. Within this study, we have taken a new approach to the analysis of FRRf data, based on a simple hypothesis; that under a given set of environmental conditions, the ratio of rate constants for RCII fluorescence emission and photochemistry falls within a narrow range, for all groups of phytoplankton. We present a simple equation, derived from the established FRRf algorithm, for determining [RCII] from dark FRRf data alone. We also describe an entirely new algorithm for estimating JVPSII, which does not require determination of [RCII] and is valid for a heterogeneous model of connectivity among RCIIs. Empirical supporting evidence is presented. These data are derived from FRR measurements across a diverse range of microalgae, in parallel with independent measurements of [RCII]. Possible sources of error, particularly under nutrient stress conditions, are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".