Iron supply and demand in the upper ocean
Bibliographic record
Abstract
Iron is hypothesized to be a limiting micronutrient for ocean primary production. This paper presents an analysis of the iron budget in the upper ocean. The global distribution of annual iron assimilation by phytoplankton was estimated from distributions of satellite‐derived oceanic primary production and measured (Fe:C)cellular ratios. The distributions of iron supply by upwelling/mixing and aeolian deposition were obtained by applying (Fe:NO3)dissolved ratios to the nitrate supply and by assuming the soluble fraction of mineral aerosols. A lower bound on the rate of iron recycling in the photic zone was estimated as the difference between iron assimilation and supply. Global iron assimilation by phytoplankton for the open ocean was estimated to be 12 × 109 mol Fe yr−1. Atmospheric deposition of total Fe is estimated to be 96×109 mol Fe yr−1 in the open ocean, with the soluble Fe fraction ranging between 1 and 10% (or 1‐10 ×109 mol Fe yr−1). By comparison, the upwelling/entrainment supply of dissolved Fe to the upper ocean is small, ∼0.7×109 mol Fe yr−1. Uncertainties in the aeolian flux and assimilation may be as large as a factor of 5‐10 but remain difficult to quantify, as information is limited about the form and transformation of iron from the soil to phytoplankton incorporation. An iron stress index, relating the (Fe:N) demand to the (Fe :N) supply, confirms the production in the high‐nitrate low‐chlorophyll regions is indeed limited by iron availability.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".