Phytoplankton Productivity in an Arctic Fjord (West Greenland): Estimating Electron Requirements for Carbon Fixation and Oxygen Production
Bibliographic record
Abstract
Accurate quantification of pelagic primary production is essential for quantifying the marine carbon turnover and the energy supply to the food web. Knowing the electron requirement (Κ) for carbon (C) fixation (ΚC) and oxygen (O2) production (ΚO2), variable fluorescence has the potential to quantify primary production in microalgae, and hereby increasing spatial and temporal resolution of measurements compared to traditional methods. Here we quantify ΚC and ΚO2 through measures of Pulse Amplitude Modulated (PAM) fluorometry, C fixation and O2 production in an Arctic fjord (Godthåbsfjorden, W Greenland). Through short- (2h) and long-term (24h) experiments, rates of electron transfer (ETRPSII), C fixation and/or O2 production were quantified and compared. Absolute rates of ETR were derived by accounting for Photosystem II light absorption and spectral light composition. Two-hour incubations revealed a linear relationship between ETRPSII and gross 14C fixation (R2 = 0.81) during light-limited photosynthesis, giving a ΚC of 7.6 ± 0.6 (mean ± S.E.) mol é (mol C)-1. Diel net rates also demonstrated a linear relationship between ETRPSII and C fixation giving a ΚC of 11.2 ± 1.3 mol é (mol C)-1 (R2 = 0.86). For net O2 production the electron requirement was lower than for net C fixation giving 6.5 ± 0.9 mol é (mol O2)-1 (R2 = 0.94). This, however, still is an electron requirement 1.6 times higher than the theoretical minimum for O2 production [i.e. 4 mol é (mol O2)-1]. The discrepancy is explained by respiratory activity and non-photochemical electron requirements and the variability is discussed. In conclusion, the bio-optical method and derived electron requirement support conversion of ETR to units of C or O2, paving the road for improved spatial and temporal resolution of primary production estimates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".