Improved estimation of carbon fixation rates from active flourometry using spectral fluorescence in light‐limited environments
Bibliographic record
Abstract
Bio‐optical models predict photosynthetic electron transport rates through photosystem II (ETRPSII) from measures of irradiance (E), the absorption coefficient of pigments associated with PSII (aPSII) that are spectrally scaled to E (a̅PSII), and the quantum efficiency of PSII (ϕ′PSII). However bio‐optical models currently suffer from methodological uncertainties in the quantification of a̅PSII, and variable stoichiometry between ETRPSII and the more ecologically‐relevant carbon fixation (PC), defined here as the quantum requirement for carbon fixation (Φe,C = ETRPSII × PC−1). Here we analyze measures of PC, ϕ′PSII, and a̅PSII across optical, thermal, nutrient and phytoplankton composition gradients in Lake Erie. We show that ϕ′PSII in the light‐limited portion of the water column is relatively constant despite the wide range of biological and environmental conditions, but that variations in a̅PSII are large. Measures of a̅PSII are shown to be highly influenced by methodology as different approaches significantly influence measures of ETRPSII and Φe,C. A new technique that derives a̅PSII from in situ spectral fluorescence measures is introduced and shown to yield ETRPSII estimates that correlate well with independent measures of PC under light limited conditions. The Φe,C inferred from this new approach agreed well with independent assessments in the lake and demonstrates that bio‐optical models with well‐parameterized a̅PSII can be usefully predictive of light‐limited PC across wide biological and chemical gradients in this great lake.
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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 | 0.001 |
| 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 | 0.001 |
| 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".