Atmospheric iron supply and marine productivity in the glacial North Pacific Ocean
Bibliographic record
Abstract
Iron (Fe) is a key element in the Earth climate system, as it can enhance marine primary productivity in the high-nutrient low-chlorophyll (HNLC) regions where, despite a high concentration of major nutrients, chlorophyll production is low due to iron limitation. Eolian mineral dust represents one of the main Fe sources to the oceans; thus, quantifying its variability over the last glacial cycle is crucial to evaluate its role in strengthening the biological carbon pump. Polar ice cores, which preserve detailed climate records in their stratigraphy, provide a sensitive and continuous archive for reconstructing past eolian Fe fluxes. Here, we show the Northern Hemisphere Fe record retrieved from the NEEM ice core (Greenland), which offers a unique opportunity to reconstruct the past Fe fluxes in a portion of the Arctic over the last 108 kyr. Holocene Fe fluxes (0.042–11.7 ka, 0.5 mg m −2 yr −1 ) at the NEEM site were 4 times lower than the average recorded over the last glacial period (11.7–108 ka, 2.0 mg m −2 yr −1 ), whereas they were greater during the Last Glacial Maximum (LGM; 14.5–26.5 ka, 3.6 mg m −2 yr −1 ) and Marine Isotope Stage 4 (MIS 4; 60–71 ka, 5.8 mg m −2 yr −1 ). Comparing the NEEM Fe record with paleoceanographic records retrieved from the HNLC North Pacific, we found that the coldest periods, characterized by the highest Fe fluxes, were distinguished by low marine primary productivity in the subarctic Pacific Ocean, likely due to the greater sea ice extent and the absence of major nutrients upwelling. This supports the hypothesis that Fe fertilization during colder and dustier periods (i.e., LGM and MIS 4) was more effective in other regions, such as the midlatitude North Pacific, where a closer relationship between marine productivity and the NEEM Fe fluxes was observed.
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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".