A model of time-dependent macromolecular and elemental composition of phytoplankton
Bibliographic record
Abstract
Phytoplankton Chl:C:N:P ratios are important from both an ecological and a biogeochemical perspective. We show that these elemental ratios can be represented by a phytoplankton physiological model of low complexity that includes major cellular macromolecular pools. In particular, our model resolves time-dependent intracellular pools of chlorophyll, proteins, nucleic acids, carbohydrates/lipids, and N and P storage. Batch culture data for two diatom and two prasinophyte species are used to constrain parameters that represent specific allocation traits and strategies. A key novelty is the simultaneous estimation of physiological parameters for two phytoplankton groups of such different sizes. The number of free parameters is reduced by assuming (i) allometric scaling for maximum uptake rates, (ii) shared half-saturation constants for synthesis of functional macromolecules, (iii) shared exudation rates of functional macromolecules across the species. The rationale behind this assumption is that across the different species, the same or similar processes, enzymes, and metabolites play a role in key physiological processes. For the turnover numbers of macromolecular synthesis and storage exudation rates, differences between diatoms and prasinophytes need to be taken into account to obtain a good fit. Our model fits suggest that the parameters related to storage dynamics dominate the differences in the C:N:P ratios between the different phytoplankton groups. Since descriptions of storage dynamics are still incomplete and imprecise, predictions of C:N:P ratios by phytoplankton models likely have a large uncertainty.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".