Phytoremediation of Cd2+ by Marine Phytoplanktons, Tetracelmis chuii and Chaetoceros calcitrans
Bibliographic record
Abstract
The use of marine phytoplankton, Tetracelmis chuii and Chaetoceros calcitrans as phytoremediator has already been reported. However their use as phytoremediator on the cadmium polluted marine has not yet well understood. Therefore, this study was conducted to evaluate the influence of the Cd2+ concentration, interacting time, and medium pH on accumulation of Cd2+ in the phytoplankton. Methods of analysis and data collection were carried out on 1) the growth rate, the number of phytoplankton cells, and the content of chlorophyl-A; 2) the Cd2+ concentration in phytoplankton at various interacting times and medium pHs; and 3) infrared spectra of phytoplankton biomasses before and after interaction with Cd2+. The growth of phytoplankton and the content of chlorophyl-A after the addition of Cd2+ into the T. Chuii medium decreased, while that after the addition of Cd2+ into the C. Calcitrans medium increased. The optimum accumulation occurred after 15 min at the pH of 8, i.e. 13.46 mg Cd2+ per g T. Chuii and 1055.27 mg per g C. Calcitrans. The functional groups of T. chuii involved in the bioaccumulation of Cd2+ are OH, -CN, S=O, N-O, S-S and M-S, while that of C. Calcitrans are OH, C=O, S-S, M-S and C=C.
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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 | 0.001 |
| 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".