UPTAKE OF CADMIUM BY FRESHWATER GREEN ALGAE: EFFECTS OF PH AND AQUATIC HUMIC SUBSTANCES<sup>1</sup>
Bibliographic record
Abstract
The effects of humic substances and low pH on short‐term Cd uptake by Pseudokirchneriella subcapitata (Korshikov) Hindak and Chlamydomonas reinhardtii Dang were investigated under defined exposure conditions. The uptake experiments were run in the presence of either a synthetic organic ligand (nitrilotriacetate) or natural organic ligands (Suwannee River fulvic or humic acid). An ion‐exchange method was used to measure the free Cd2+ concentrations in the exposure solutions. At pH 5, measured free Cd2+ concentrations agreed with estimations made using the geochemical equilibrium model WHAM, but at pH 7 the model overestimated complexation by both Suwannee River fulvic and humic acids compared with the ion‐exchange measurements. Consistent with the metal internalization step being rate limiting for overall short‐term uptake, intracellular Cd uptake was linear for exposure times less than 20 min at pH 5 or pH 7 for both algal species. After taking into account complexation of Cd in solution, Suwannee River humic substances had no additional effects on cadmium uptake at pH 7, as would be predicted by the free ion model. This absence of effects other than complexation persisted at pH 5, where the tendency of humic substances to adsorb to the algal cell surface is favored. Changes in pH strongly influenced Cd uptake, with the intracellular flux of Cd being at least 20 times lower at pH 5 than at pH 7 for P. subcapitata. Our results support models such as the free ion model or the biotic ligand model, in which humic substances act indirectly on Cd uptake by reducing the bioavailability of Cd by complexation in solution.
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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) | 0.001 | 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".