THE EFFECTS OF IRON AND COPPER AVAILABILITY ON THE COPPER STOICHIOMETRY OF MARINE PHYTOPLANKTON<sup>1</sup>
Bibliographic record
Abstract
We studied the interactive effects of iron (Fe) and copper (Cu) availability on the growth rates, Cu quotas, and steady-state Cu-uptake rates (ρss Cu) of 12 phytoplankton (from four classes and two marine environments). A mixed-effect statistical model indicated that low Fe significantly decreased phytoplankton growth rates. In contrast, lowering Cu levels only decreased the growth rates of the oceanic phytoplankton. Under Fe/Cu sufficiency, the Cu quotas ranged from 0.36 to 3.8 μmol Cu · mol(-1) C. Copper levels in the growth medium had a significant positive effect on the Cu quotas, and this effect was dependent on the algal class. Under Fe/Cu sufficiency, the highest average Cu quotas were observed for the Bacillariophyceae, followed by the Cyanophyceae, Prymnesiophyceae, and lastly the Dinophyceae. Similar taxonomic trends were observed for the ρss Cu. Although the Cu:C ratios were not significantly higher in oceanic strains, there are five independent lines of evidence supporting a more important role of Cu in the physiology of the oceanic phytoplankton. The mixed-effect model indicated a significant Cu effect on the growth rates and ρss Cu of the oceanic strains, but not the coastal strains. In addition, lowering the Cu concentration in the media decreased the Cu quotas and ρss Cu of the oceanic strains to a greater extent (5.5- and 5.4-fold, respectively) than those of the coastals (3.8- and 4.7-fold, respectively). Iron limitation only had a significant effect on the Cu quotas of the oceanic strains, and this effect was dependent on Cu level and taxonomic class. Our results highlight a complex physiological interaction between Fe and Cu in marine phytoplankton.
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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 | 0.001 | 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) | 0.001 | 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".