Extracting phytoplankton physiological traits from batch and chemostat culture data
Bibliographic record
Abstract
Abstract As the role of phytoplankton diversity in ocean biogeochemistry becomes widely recognized, the description of plankton in ocean ecological models is becoming more sophisticated. This means that a growing number of plankton physiological traits need to be determined for various species and under various growth conditions. We investigate how these traits can be estimated efficiently from common batch culture and chemostat experiments. We use the Metropolis algorithm, a random‐walk Monte Carlo method, to estimate phytoplankton parameter values, along with the uncertainties in these values. First, we fit plankton physiological models to high‐resolution batch culture and chemostat data sets to obtain parameter sets that are as accurate as possible. Then, we subsample these data sets and assess to which extent the accuracy is sacrificed when fewer measurements are taken. Two measurement points within the exponential growth stage of the batch culture data set are sufficient to constrain the maximum protein synthesis rate, the maximum photosynthesis rate, and the chlorophyll‐to‐nitrogen ratio. Two measurements during the stationary phase of the batch culture experiment are then enough to constrain the parameters related to carbon excretion and the photoacclimation time. From the chemostat experiment, only four measured points are needed to constrain the parameters connected with the internal reserve dynamics of phytoplankton. Thus, we demonstrate that traits related to key biogeochemical and physiological processes can be determined with only a few batch culture and chemostat measurements, as long as the measurement points are selected appropriately.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".