Nitrogen and phosphorus differentially control marine biomass production and stoichiometry
Bibliographic record
Abstract
Globally averaged, surface particulate nitrogen and phosphorus approximate the 16:1, N:P “Redfield ratio.” In observations, N:P ratios vary latitudinally at ranges attributable to both phytoplankton community composition and physiological acclimation, but their relative contributions to the N:P ratio remain unclear. Here, results from a 29-day mesocosm experiment highlight how inorganic nitrogen and/or phosphorus nutrient supply can affect the bulk particle stoichiometry of a North Pacific Subtropical Gyre plankton community. Nitrogen additions, with and without phosphorus, increase total productivity and diatom abundance, whereas treatments with just phosphorus additions remain similar to the no-nutrient addition control. Continual nitrogen supply without phosphorus results in higher particulate N:P ratios than expected based on the phytoplankton community present. Several P-stress markers identified in those treatments highlight the importance of acclimation in extending particulate N:P ratios beyond the Redfield ratio. Phytoplankton’s ability to maintain growth under P-stress conditions has implications for global carbon cycling. Mesocosm experiments revealed that both phytoplankton community composition and cellular acclimation influence marine particulate C:N:P ratios, with community shifts more sensitive to nitrogen supply and acclimation to the nutrient N:P supply ratio
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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 | 0.001 |
| 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".