Development of multispecies algal bioassays using flow cytometry
Bibliographic record
Abstract
Multispecies algal bioassays, suitable for assessing copper toxicity, were developed with three marine (Micromonas pusilla, Phaeodactylum tricornutum, and Heterocapsa niei) and three freshwater (Microcystis aeruginosa, Pseudokirchneriella subcapitata, and Trachelomonas sp.) microalgae. Flow cytometry was used to separate and count algal signals based on pigment fluorescence and cell size. Species were mixed together on the basis of equivalent surface areas to avoid the confounding effect on toxicity of increased biomass for metal binding. Under control conditions (no added copper), M. pusilla growth was inhibited in the presence of the other marine microalgae compared to single-species tests, while the opposite was true (i.e., growth stimulation) for M. aeruginosa and P. subcapitata in freshwater mixtures. Competition for nutrients, including CO2, and algal exudate production may account for these effects. Interactions between microalgal species also had a significant effect on copper toxicity to some species. In freshwater multispecies bioassays, the toxicity of copper to Trachelomonas sp. was greater in the presence of other species, with copper concentrations required to inhibit growth (cell division) rate by 50% (72-h [IC50]) decreasing from 9.8 to 2.8 microg Cu/L in single- and multispecies bioassays, respectively. In contrast, in marine multispecies bioassays, copper toxicity to the marine diatom P. tricornutum was reduced compared to single-species bioassays, with an increase in the 72-h IC50 value from 13 to 24 microg Cu/L. This reduction in copper toxicity was not explained by differences in the copper complexing capacity in solution (as a result of exudate production) because labile copper, measured by anodic stripping voltammetry, was similar for P. tricornutum alone and in the mixture. These results demonstrate that single-species bioassays may over- or underestimate metal toxicity in natural waters.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".