The interaction between inorganic iron and cadmium uptake in the marine diatom Thalassiosira oceanica
Bibliographic record
Abstract
We examined substrate‐saturated Fe(II) vs. Fe(III) uptake rates of Thalassiosira oceanica preconditioned to varying degrees of Fe limitation. Inorganic Fe(III) uptake rates by Fe‐sufficient T. oceanica were 2.4‐fold faster than the corresponding inorganic Fe(II) uptake rates. However, when cultures were severely Fe limited, the rate of Fe(II) uptake was upregulated 15‐fold, while that of Fe(III) uptake increased only fivefold. The interactions between substrate‐saturated uptake rates of inorganic Cd(II) and either Fe(II) or Fe(III) by Fe‐limited T. oceanica were also investigated. The addition of equimolar Cd(II) concentration to the Fe(II) uptake media resulted in a ~50% reduction of inorganic Fe(II) uptake rates compared with those in Cd‐free media. In turn, Cd uptake rates were inhibited ~36% in the presence of an equimolar Fe(II) concentration. In contrast to Fe(II), Fe(III) transport exhibited no interaction with Cd(II). T. oceanica thus has separate transporters for inorganic Fe(III) and Fe(II). Cadmium(II) and Fe(II) appear to enter the cell through a common putative divalent metal transporter that is upregulated under Fe deficiency. The interaction of Fe(II) and Cd(II) transport under Fe deficiency provides a plausible mechanism to explain some laboratory and field observations of higher Cd quotas in Fe‐limited phytoplankton.
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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 | 0.001 |
| 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".