Iron and silicic acid effects on phytoplankton productivity, diversity, and chemical composition in the central equatorial Pacific Ocean
Bibliographic record
Abstract
A microcosm nutrient‐amendment experiment using central equatorial Pacific Ocean (0°, 140°W) mixed‐layer waters was conducted to determine biogeochemical controls on phytoplankton with an emphasis on post‐iron enrichment nutrient uptake dynamics and species composition. The addition of either Fe (termed Fe‐only) or Fe and Si(OH)4 (termed FeSi) to on‐deck incubations resulted in growth primarily of pennate diatoms, with statistically equivalent increases relative to the control in maximum photochemical efficiency, chlorophyll a (Chl a) concentrations, particulate organic carbon and nitrogen concentrations, and dissolved inorganic carbon uptake rates. In contrast, at peak Chl a concentrations, there was a 3.4‐fold higher abundance of large diatoms and a 3.9‐ fold lower abundance of small pennate diatoms in FeSi relative to Fe‐only, which translated into a 3.5‐fold higher Si(OH)4 uptake rate and a 2.1‐fold higher biogenic silica concentration. Fourier transform infrared spectroscopy indicated that relative to cells from Fe‐only, cells from FeSi possessed the lowest protein : carbohydrate ratios, and ratios of lipids, proteins, and carbohydrates relative to silica, consistent with differences in diatom C allocation or increased silicification or both. Our results suggest that after Fe addition, diatom organic matter accumulation rates (i.e., C and N uptake rates) are enhanced but the low, ambient [Si(OH)4] retards cell division rates, resulting in fewer large diatoms with relatively high C and N contents. After the simultaneous addition of Fe and Si(OH)4, enhanced rates of diatom organic matter accumulation and cell division results in more large, heavily silicified diatoms with relatively low C and N contents.
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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) | 0.000 | 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".