Insights into Cellular Localization and Environmental Influences on the Toxicity of Marine Fish-Killing Flagellate, Heterosigma akashiwo
Bibliographic record
Abstract
Heterosigma akashiwo is a unicellular microalga which can cause massive mortality in both wild and cultivated fish worldwide, resulting in substantial economic losses. Environmental parameters such as salinity, light, and temperature showed a significant effect on bloom initiation and the toxicity of H. akashiwo. While in previous studies a one-factor-at-a-time (OFAT) approach was utilized, which only changes one variable at a time while keeping others constant, in the current study a more precise and effective design of experiment (DOE) approach, was used to investigate the simultaneous effect of three factors and their interactions. The study employed a central composite design (CCD) to investigate the effect of salinity, light intensity, and temperature on the toxicity, lipid, and protein production of H. akashiwo. A yeast cell assay was developed to assess toxicity, which offers rapid and convenient cytotoxicity measurements using a lower volume of samples compared to conventional methods using the whole organism. The obtained results showed that the optimum condition for toxicity of H. akashiwo was 25 °C, a salinity of 17.5, and a light intensity of 250 μmol photons m−2 s−1. The highest amount of lipid and protein was found at 25 °C, a salinity of 30, and a light intensity of 250 μmol photons m−2 s−1. Consequently, the combination of warm water mixing with lower salinity river input has the potential to enhance H. akashiwo toxicity, which aligns with environmental reports that establish a correlation between warm summers and extensive runoff conditions that indicate the greatest concern for aquaculture facilities.
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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 | 0.000 | 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".