Probing the Bioavailability of Dissolved Iron to Marine Eukaryotic Phytoplankton Using In Situ Single Cell Iron Quotas
Bibliographic record
Abstract
Abstract We present a new approach for quantifying the bioavailability of dissolved iron (dFe) to oceanic phytoplankton. Bioavailability is defined using an uptake rate constant (k in‐app ) computed by combining data on: (a) Fe content of individual in situ phytoplankton cells; (b) concurrently determined seawater dFe concentrations; and (c) growth rates estimated from the PISCES model. We examined 930 phytoplankton cells, collected between 2002 and 2016 from 45 surface stations during 11 research cruises. This approach is only valid for cells that have upregulated their high‐affinity Fe uptake system, so data were screened, yielding 560 single cell k in‐app values from 31 low‐Fe stations. We normalized k in‐app to cell surface area (S.A.) to account for cell‐size differences. The resulting bioavailability proxy ( k in‐app /S.A.) varies among cells, but all values are within bioavailability limits predicted from defined Fe complexes. In situ dFe bioavailability is higher than model Fe‐siderophore complexes and often approaches that of highly available inorganic Fe′. Station averaged k in‐app /S.A. are also variable but show no systematic changes across location, temperature, dFe, and phytoplankton taxa. Given the relative consistency of k in‐app /S.A. among stations (ca. five‐fold variation), we computed a grand‐averaged dFe availability, which upon normalization to cell carbon (C) yields k in‐app /C of 42,200 ± 11,000 L mol C −1 d −1 . We utilize k in‐app /C to calculate dFe uptake rates and residence times in low Fe oceanic regions. Finally, we demonstrate the applicability of k in‐app /C for constraining Fe uptake rates in earth system models, such as those predicting climate mediated changes in net primary production in the Fe‐limited Equatorial Pacific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".