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 (ETR PSII ) from measures of irradiance (E), the absorption coefficient of pigments associated with PSII (a PSII ) that are spectrally scaled to E (a̅ PSII ), and the quantum efficiency of PSII (ϕ′ PSII ). However bio‐optical models currently suffer from methodological uncertainties in the quantification of a̅ PSII , and variable stoichiometry between ETR PSII and the more ecologically‐relevant carbon fixation (P C ), defined here as the quantum requirement for carbon fixation (Φ e,C = ETR PSII × P C −1 ). Here we analyze measures of P C , ϕ′ PSII , and a̅ PSII 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 a̅ PSII are large. Measures of a̅ PSII are shown to be highly influenced by methodology as different approaches significantly influence measures of ETR PSII and Φ e,C . A new technique that derives a̅ PSII from in situ spectral fluorescence measures is introduced and shown to yield ETR PSII estimates that correlate well with independent measures of P C under light limited conditions. The Φ e,C inferred from this new approach agreed well with independent assessments in the lake and demonstrates that bio‐optical models with well‐parameterized a̅ PSII can be usefully predictive of light‐limited P C across wide biological and chemical gradients in this great lake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".