Ocean acidification enhances the growth rate of larger diatoms
Bibliographic record
Abstract
Ocean acidification is changing the nature of inorganic carbon availability in the global oceans. Diatoms account for µ 40% of all marine primary productivity and are major contributors to the export of atmospheric carbon to the deep ocean. Larger diatoms are more likely to be stimulated by future increases in CO2 availability as a result of their low surface area to volume ratio and lower diffusive flux of CO2 relative to their carbon demand for growth. Here we quantify the effect of the partial pressure of carbon dioxide (PCO2), at levels of 190, 380, and 750 µL L−1, on the growth rate, photosystem II electron transport rate (ETR), and elemental composition for five diatom species ranging over five orders of magnitude in cell volume. Growth rates for all species were enhanced under 750 relative to 190 and 380 µL L−1, with little change in ETR or elemental stoichiometries, indicating an enhanced allocation of photochemical energy to growth under elevated PCO2. PCO2 enhancement of growth rates was size dependent. Under 750 vs. 190 µL L−1 partial pressures, growth rate was enhanced by µ 5% for the smaller diatom species to µ 30% for the largest species examined. The size dependence of CO2‐stimulated growth enhancement indicates that ocean acidification may selectively favor an increase in the growth rates of larger vs. smaller phytoplankton species in the sea, with potentially significant consequences for carbon biochemistry.
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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 | 0.000 | 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) | 0.001 | 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".