Influence of essential elements on cadmium uptake and toxicity in a unicellular green alga: The protective effect of trace zinc and cobalt concentrations
Bibliographic record
Abstract
Within the biotic ligand model (BLM) construct, major cations are considered to be simple competitors for metal binding to uptake sites and may offer some protection against metal-induced toxicity, but the influence of essential trace elements and cell preconditioning to different micronutrient concentrations on metal uptake and toxicity is considered negligible. To test these underlying assumptions, we monitored Cd uptake and toxicity in a green alga (Chlamydomonas reinhardtii) after long-term exposures (60 h) to a range of environmentally realistic free Zn(2+) , Co(2+) , Fe(3+) , Mn(2+) , Ca(2+) , and Cu(2+) concentrations buffered with nitrilotriacetic acid. A 200-fold increase in free [Mn(2+) ] as well as a 100-fold increase in free [Fe(3+) ] did not affect Cd uptake or toxicity, whereas a 50-fold increase in free [Ca(2+) ] effectively offered some protection, as predicted by the BLM. However, a 10-fold increase in free [Cu(2+) ] significantly enhanced Cd toxicity by a factor of approximately 2, whereas a 100-fold increase in free [Zn(2+) ] and [Co(2+) ] from 10(-11) to 10(-9) M significantly decreased Cd uptake and toxicity by more than twofold. These effects did not change with prior algal acclimation to different essential micronutrient concentrations. Low essential trace metal concentrations may strongly affect the uptake and toxicity of Cd in freshwater algae and should be taken into consideration in future developments of the BLM.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".