Predicting Cadmium Accumulation and Toxicity in a Green Alga in the Presence of Varying Essential Element Concentrations Using a Biotic Ligand Model
Bibliographic record
Abstract
This study refines the Biotic Ligand Model (BLM) approach by integrating the modulating effects of various essential elements on cadmium (Cd) uptake kinetics in the freshwater alga Chlamydomonas reinhardtii. The algae were first acclimated to a low (LM) or high trace metal (HM) medium as well as to low or high free Cd(2+) and Co(2+) concentrations. The short-term Cd transport capacity and affinity were then quantified in exposure media in which essential trace metals and calcium concentrations were manipulated. The results show that after acclimation to the LM medium, exposure to high free Ca(2+) decreases the capacity of the Cd transport system. Also, acclimation to high (10(-9) M free Co(2+)) or low (10(-11) M free Co(2+)) did not significantly affect Cd uptake rates. When all essential trace metals were simultaneously increased in the acclimation (and exposure) medium, the capacity of the transport system decreased by ∼ 60%, a decrease close to that due to high [Zn(2+)] alone, suggesting that Zn is the main trace metal modulator of the Cd transporter capacity. Changes in Cd toxicity (growth inhibition) in the presence of different essential trace metal concentrations were strongly related to the steady-state concentration of intracellular cadmium, regardless of the cell's nutritional state. Our BLM incorporating the physiological effects of Ca(2+) and other trace metals predicts steady-state Cd accumulation in the presence of varying concentrations of essential elements at 7 nM free Cd(2+), but predictions over a wide range of free [Cd(2+)] proved to be more difficult.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".