Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean
Bibliographic record
Abstract
Abstract. The element silicon (Si) is required for the growth of silicified organisms in marine environments, such as diatoms. These organisms consume vast amounts of Si together with N, P, and C, connecting the biogeochemical cycles of these elements. Thus, understanding the Si cycle in the ocean is critical for understanding wider issues such as carbon sequestration by the ocean's biological pump. In this review, we show that recent advances in process studies indicate that total Si inputs and outputs, to and from the world ocean, are 57 % and 37 % higher, respectively, than previous estimates. We also update the total ocean silicic acid inventory value, which is about 24 % higher than previously estimated. These changes are significant, modifying factors such as the geochemical residence time of Si, which is now about 8000 years, 2 times faster than previously assumed. In addition, we present an updated value of the global annual pelagic biogenic silica production (255 Tmol Si yr−1) based on new data from 49 field studies and 18 model outputs, and we provide a first estimate of the global annual benthic biogenic silica production due to sponges (6 Tmol Si yr−1). Given these important modifications, we hypothesize that the modern ocean Si cycle is at approximately steady state with inputs =14.8(±2.6) Tmol Si yr−1 and outputs =15.6(±2.4) Tmol Si yr−1. Potential impacts of global change on the marine Si cycle are discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".