Glacial Dust Surpasses Both Volcanic Ash and Desert Dust in Its Iron Fertilization Potential
Bibliographic record
Abstract
Abstract The subarctic Pacific Ocean and Bering Sea comprise the second‐largest high nitrate, low chlorophyll region in the world, where primary production is limited by the availability of iron (Fe). To estimate the potential impact of different terrestrial aerosol Fe sources on marine ecosystems, we performed a suite of laboratory assessments following established protocols, including: (1) leaching with Milli‐Q water, (2) sequential extractions, (3) complete acid digestions, (4) X‐ray diffraction, and (5) grain size analysis. Measurements were performed on 20 fine‐grained (<5 μm) glacier‐derived sediments from Alaska and the Yukon, two fresh, never‐wetted volcanic ashes (Redoubt 2009 and Pavlof 2016), and six weathered ashes (Redoubt and Augustine) which span the past ∼8,700 years. We compared results to published data on Asian desert‐derived sediments, finding that the glacier‐derived sediments have five times higher easily reducible Fe (median 2.3 ± 0.6 wt.%) than desert‐derived samples (0.49 ± 0.1 wt.%) and fourteen times higher easily reducible Fe than fresh ash (0.16 ± 0.1 wt.%). In addition, fractional Fe solubility was higher in glacial sediment (median cumulative 0.31 ± 0.11% Fe S ) than volcanic ash (0.04 ± 0.02% Fe S ). Glacial sediments contained higher concentrations of other bioactive metals including Co, Ni, Cu, Zn, Mo, Cd, and Pb. Results suggest that glacier‐derived dust may provide the subarctic Pacific with more bioavailable iron per unit mass than either volcanic ash or Asian desert‐derived dust.
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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) | 0.001 | 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".