COPPER‐UPTAKE KINETICS OF COASTAL AND OCEANIC DIATOMS<sup>1</sup>
Bibliographic record
Abstract
We investigated copper (Cu) acquisition mechanisms and uptake kinetics of the marine diatoms Thalassiosira oceanica Hasle, an oceanic strain, and Thalassiosira pseudonana Hasle et Heimdal, a coastal strain, grown under replete and limiting iron (Fe) and Cu availabilities. The Cu‐uptake kinetics of these two diatoms followed classical Michaelis–Menten kinetics. Biphasic uptake kinetics as a function of Cu concentration were observed, suggesting the presence of both high‐ and low‐affinity Cu‐transport systems. The half‐saturation constants (Km) and the maximum Cu‐uptake rates (Vmax) of the high‐affinity Cu‐transport systems (∼7–350 nM and 1.5–17 zmol · μm−2 · h−1, respectively) were significantly lower than those of the low‐affinity systems (>800 nM and 30–250 zmol · μm−2 · h−1, respectively). The two Cu‐transport systems were controlled differently by low Fe and/or Cu. The high‐affinity Cu‐transport system of both diatoms was down‐regulated under Fe limitation. Under optimal‐Fe and low‐Cu growth conditions, the Km of the high‐affinity transport system of T. oceanica was lower (7.3 nM) than that of T. pseudonana (373 nM), indicating that T. oceanica had a better ability to acquire Cu at subsaturating concentrations. When Fe was sufficient, the low‐affinity Cu‐transport system of T. oceanica saturated at 2,000 nM Cu, while that of T. pseudonana did not saturate, indicating different Cu‐transport regulation by these two diatoms. Using CuEDTA as a model organic complex, our results also suggest that diatoms might be able to access Cu bound within organic Cu complexes.
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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 | 0.000 | 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 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".