Insights into the bioavailability of oceanic dissolved Fe from phytoplankton uptake kinetics
Bibliographic record
Abstract
. The bioavailability of dFe in seawater is however difficult to appraise since it is bound by a variety of poorly characterized organic ligands. Here, we propose a new approach for evaluating seawater dFe bioavailability based on its uptake rate constant by Fe-limited cultured phytoplankton. We utilized seven phytoplankton species of diverse classes, sizes, and provenances to probe for dFe bioavailability in 12 seawater samples from several ocean basins and depths. All tested phytoplankton acquired organically bound Fe in any given sample at similar rates (after normalizing to cellular surface area), confirming that multiple, Fe-limited phytoplankton species can be used to probe dFe bioavailability in seawater. These phytoplankton-based uptake rate constants allowed us to compare water types, and obtain a grand average estimate of seawater dFe bioavailability. Among water types, dFe bioavailability varied by approximately four-fold, and did not clearly correlate with Fe concentrations or any of the measured Fe speciation parameters. Compared with well-studied Fe complexes, seawater dFe is more available than model siderophore Fe, but less available than inorganic Fe. Exposure of seawater to sunlight, however, significantly enhanced dFe bioavailability. The rate constants established in this work, not only facilitate comparison between water types, but also allow calculation of Fe uptake rates by phytoplankton in the ocean based on measured dFe concentrations. The approach established and verified in this study, opens a new way for determining dFe bioavailability in samples across the ocean, and enables modeling of in situ Fe uptake rates by phytoplankton using dFe concentrations from GEOTRACES datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".