Particulate trace metal dynamics in response to increased CO <sub>2</sub> and iron availability in a coastal mesocosm experiment
Bibliographic record
Abstract
Abstract. Rising concentrations of atmospheric carbon dioxide are causing ocean acidification and will influence marine processes and trace metal biogeochemistry. The importance of the combined impacts of elevated CO2 and changes in trace metal availability on marine plankton remain largely unknown. A mesocosm experiment was performed to study changes in particulate trace metal concentrations during a bloom dominated by the coccolithophore Emiliania huxleyi. We employed a full-factorial experimental design, comprising all combinations of ambient and elevated pCO2 and dissolved iron (dFe). Particulate metal concentrations (Fe, Cu, Zn, Co, Mn, Cd, Mo, Ti and Pb) were determined by high-resolution inductively coupled plasma mass spectrometry (HR-ICPMS). We examined biogenic and lithogenic sources of particulate metals, and their evolution during the experiment. Biogenic metal concentrations were estimated from bulk particle measurements by comparing phosphorus (P)-normalised quotas with published ratios, as well as concentrations of particulate trace metals in the presence and absence of an oxalate-EDTA wash. Our results demonstrate that particulate Ti and Fe concentrations were dominated by lithogenic material in the fjord. In contrast, particulate Cu, Co, Mn, Zn, Mo and Cd concentrations correlated with P concentrations and phytoplankton biomass, indicative of their strong biogenic character. Furthermore, ocean acidification changed the relative concentrations of particulate metals; a result mainly driven by the effects of ocean acidification on the growth of different phytoplankton phyla. This study demonstrates the utility and robustness of combining trace metal analyses of particles in a controlled mesocosm experiment with manipulations of CO2 and Fe concentrations using natural assemblages of marine 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".