Distinct drivers of two size fractions of operationally dissolved <scp>iron</scp> in a temperate river
Bibliographic record
Abstract
Abstract Riverine dissolved iron (Fe) affects water color, nutrients, and marine carbon cycling. Fe size and coupling with dissolved organic matter (DOM), in part, modulates the biogeochemical roles of riverine Fe. We used size fractionation to operationally define dissolved Fe (< 0.22 μm) into soluble (< 0.02 μm) and colloidal (0.02–0.22 μm) fractions in order to characterize the downstream drivers, concentrations, and fluxes of Fe across season and hydrologic regime at the freshwater Connecticut River mainstem, which we sampled bi‐weekly for 2 yrs. Drivers of colloidal and soluble Fe concentrations were markedly different. The response of colloidal Fe concentration to changes in discharge was modulated by water temperature; colloidal Fe decreased with increasing discharge at temperatures < 10.5°C, but increased with increasing discharge at temperatures > 10.5°C. Conversely, soluble Fe concentrations were only positively correlated to discharge at high temperatures (> 20°C). Soluble Fe was strongly positively correlated to a humic‐like DOM fluorescence component, suggesting coupling with DOM subsets, potentially through complexation. While average colloidal Fe fluxes varied twofold seasonally, soluble Fe fluxes varied ninefold; therefore, soluble Fe variability was more important to the overall dissolved Fe variability than colloidal Fe, despite lower concentrations. Seasonal Fe fluxes were decoupled from discharge: dissolved and soluble Fe fluxes were greatest in the fall, whereas discharge was greatest in the spring. Fluxes of soluble Fe, which may be more bioavailable and more likely to be exported to the ocean, were lowest in the summer when downstream biological demand is high, having implications for primary productivity and iron uptake.
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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) | 0.000 | 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".