Iron in the NEEM ice core relative to Asian loess records over the last glacial–interglacial cycle
Bibliographic record
Abstract
Abstract Mineral dust can indirectly affect the climate by supplying bioavailable iron (Fe) to the ocean. Here, we present the records of dissolved Fe (DFe) and total Fe (TDFe) in North Greenland Eemian Ice Drilling (NEEM) ice core over the past 110 kyr BP. The Fe records are significantly negatively correlated with the carbon-dioxide (CO2) concentrations during cold periods. The results suggest that the changes in Fe fluxes over the past 110 kyr BP in the NEEM ice core are consistent with those in Chinese loess records because the mineral-dust distribution is controlled by the East Asian deserts. Furthermore, the variations in the dust input on a global scale are most likely driven by changes in solar radiation during the last glacial–interglacial cycle in response to Earth's orbital cycles. In the last glacial–interglacial cycle, the DFe/TDFe ratios were higher during the warm periods (following the post-Industrial Revolution and during the Holocene and last interglacial period) than during the main cold period (i.e. the last glacial maximum (LGM)), indicating that the aeolian input of iron and the iron fertilization effect on the oceans have a non-linear relationship during different periods. Although the burning of biomass aerosols has released large amounts of DFe since the Industrial Revolution, no significant responses are observed in the DFe and TDFe variations during this period, indicating that severe anthropogenic contamination has no significant effect on the DFe (TDFe) release in the NEEM ice core.
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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 | 0.001 | 0.001 |
| 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".