The upper ocean silicon cycle of the subarctic Pacific during the EXPORTS field campaign
Bibliographic record
Abstract
Diatoms are major contributors to marine primary productivity and carbon export due to their rapid growth in high-nutrient environments and their heavy silica ballast. Their contributions are highly modified in high-nutrient low-chlorophyll regions due to the decoupling of upper-ocean silicon and carbon cycling caused by low iron (Fe). The Si cycle and the role of diatoms in the biological carbon pump was examined at Ocean Station Papa (OSP) in the HNLC region of the northeastern subarctic Pacific during the NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field study. Sampling occurred during the annual minimum in surface silicic acid (Si(OH)4) concentration. Biogenic silica (bSi) concentrations were low, being in the tens of nanomolar range, despite high Si(OH)4 concentrations of about 15 μM. On average, the >5.0-µm particle size fraction dominated Si dynamics, accounting for 65% of bSi stocks and 81% of Si uptake compared to the small fraction (0.6–5.0 μm). Limitation of Si uptake was detected in the small, but not the large, size fraction. Growth rate in small diatoms was limited by Fe, while their Si uptake was restricted by Si(OH)4 concentration, whereas larger diatoms were only growth-limited by Fe. About a third of bSi production was exported out of the upper 100 m. The contribution of diatoms to carbon export (9–13%) was about twice their contribution to primary productivity (3–7%). The combination of low bSi production, low diatom primary productivity and high bSi export efficiency at OSP was more similar to the dynamics in the subtropical gyres than to other high-nutrient low-chlorophyll regions.
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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".