Size-scaling of macromolecules and chemical energy content in the eukaryotic microalgae
Bibliographic record
Abstract
The macromolecular composition and cell size of microalgae can influence their competitive interactions for nutrients and food quality for predators. Here we quantify the cell volume and dry weight based size-scaling of protein, lipid, carbohydrate and chemical energy content of eukaryotic microalgae from data extracted from the scientific literature. Across all the microalgae examined, cell size is an excellent predictor of macromolecular and chemical energy content with size-scaling exponents ranging from 0.8 to 0.93 for cell volume and 0.96 to 1.1 for dry weight. There are second-order taxonomic differences in the size scaling of macromolecular and chemical energy content. Relative to the green algae and dinoflagellates, the diatoms have lower cell volume size-scaling exponents for protein, lipid and chemical energy content due to their larger increase in vacuole volume with increasing cell volume. The dinoflagellates have a lower size-scaling exponent for carbohydrate relative to the diatoms and green algae and the green algae have a relatively high size-scaling exponent for protein as compared to the diatoms. Differences in the size-scaling of macromolecular and chemical energy content across the diatoms, green algae and dinoflagellates appear to reflect fundamental differences in cellular architecture and growth and storage allocation strategies across these microalgal phyla.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".