The relationship between cellular protein content and selenium accumulation in freshwater microalgae
Bibliographic record
Abstract
Variability in the bioconcentration of selenium (Se) by primary producers at the base of the food web results in uncertainty in predictions of bioaccumulation and ecological risk to higher trophic level organisms. Water chemistry, speciation of Se, and periphyton community composition have all been suggested as factors that contribute to variability in bioconcentration by primary producers; however, the role of physiological composition of periphyton species in influencing the bioconcentration of Se has not been previously evaluated. To determine if a relationship exists between algal protein content and Se accumulation, Parachlorella kessleri, Chlorella vulgaris, and Raphidocelis subcapitata were exposed to Se (as selenate) and analyzed for total protein and tissue Se content in the exponential and stationary growth phases. Protein content and Se accumulation in R. subcapitata in the stationary phase were also measured under two light intensities. No relationship between cellular protein content and Se accumulation was found for algae in the exponential phase; however, a strong relationship was found in the stationary phase among species and for R. subcapitata under differing light intensities. Absolute Se accumulations by P. kessleri, C. vulgaris, and R. subcapitata in the stationary phase were statistically different; however, the concentrations of Se in protein were similar across species. These results suggest that cellular protein content in microalgae influences Se bioconcentration and that algal protein content may improve Se bioaccumulation modeling in food webs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".