A call for refining the role of humic-like substances in the oceanic iron cycle
Bibliographic record
Abstract
is sequestered in the ocean, but this requires iron, which is in scarce supply. As over 99% of iron is complexed to organic ligands, which increase iron solubility and microbial availability, understanding the processes governing ligand dynamics is of fundamental importance. Ligands within humic-like substances have long been considered important for iron complexation, but their role has never been explained in an oceanographically consistent manner. Here we show iron co-varying with electroactive humic substances at multiple open ocean sites, with the ratio of iron to humics increasing with depth. Our results agree with humic ligands composing a large fraction of the iron-binding ligand pool throughout the water column. We demonstrate how maximum dissolved iron concentrations could be limited by the concentration and binding capacity of humic ligands, and provide a summary of the key processes that could influence these parameters. If this relationship is globally representative, humics could impose a concentration threshold that buffers the deep ocean iron inventory. This study highlights the dearth of humic data, and the immediate need to measure electroactive humics, dissolved iron and iron-binding ligands simultaneously from surface to depth, across different ocean basins.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".