Unravelling Metal Speciation in the Microenvironment Surrounding Phytoplankton Cells to Improve Predictions of Metal Bioavailability
Bibliographic record
Abstract
Abstract A lack of knowledge on metal speciation in the microenvironment surrounding phytoplankton cells (i.e., the phycosphere) represents an impediment to accurately predicting metal bioavailability. Phycosphere pH and O2 concentrations from a diversity of algae species were compiled. For marine algae in the light, the average increases were 0.32 pH units and 0.17 mM O2 in the phycosphere, whereas in the dark the average decreases were 0.10 pH units and 0.03 mM O2, in comparison to bulk seawater. In freshwater algae, the phycosphere pH increased by 1.28 units, whereas O2 increased by 0.38 mM in the light. Equilibrium modeling showed that the pH alteration influenced the chemical species distribution (i.e., free ion, inorganic complexes, and organic complexes) of Al, Cd, Co, Cu, Fe, Hg, Mn, Ni, Pb, Sc, Sm, and Zn in the phycosphere, and the O2 fluctuation increased oxidation rates of Cu(I), Fe(II) and Mn(II) from 2 to 938-fold. The pH/O2-induced changes in phycosphere metal chemistry were larger for freshwater algae than for marine species. Reanalyses of algal metal uptake data in the literature showed that uptake of the trivalent metals (Sc, Sm and Fe), in addition to divalent metals, can be better predicted after considering the phycosphere chemistry.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".