Comparison of Cd, Cu, and Zn toxic effects on four marine phytoplankton by pulse-amplitude-modulated fluorometry
Bibliographic record
Abstract
The toxic effects of Cd, Cu, and Zn on four different marine phytoplankton, Dunaliella tertiolecta, Prorocentrum minimum, Synechococcus sp., and Thalassiosira weissflogii, were examined by comparing the cell-specific growth rate, pulse-amplitude-modulated (PAM) parameters (maximum photosystem II quantum yield phiM and operational quantum yield phi'M, chlorophyll a content, and cellular metal concentration, over a 96-h period. The calculated no-observed-effect concentration (NOEC) based on both cell-specific growth rate and two PAM parameters (phiM and phi'M) were mostly identical. Thus, these PAM parameters and cell-specific growth rate were comparable in their sensitivities as the biomarkers for trace metal toxicity to marine phytoplankton. The cyanobacteria Synechococcus sp. was the most sensitive species among the four algal species tested because of its higher cell surface to volume ratio. The toxicity of the three tested metals followed the order of Cd > Cu > Zn based on the cellular metal concentration of the four algae at the NOEC. The cellular metal bioaccumulation followed the same Freundlich isotherm for each metal regardless of the algal species, indicating that the metal accumulation was a nonmetabolic process under high ambient metal concentrations and that the cell surface metal binding was comparable among the different species. For all the algae examined in our study, the bioaccumulation potentials of Cu and Zn were similar to each other, while the Cd bioaccumulation was much lower under environmentally realistic metal concentration.
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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 | 0.001 | 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".