Influence of multi‐stressor combinations of <scp><i>p</i>CO<sub>2</sub></scp>, temperature, and salinity on the toxicity of <i>Heterosigma akashiwo</i> (Raphidophyceae), a fish‐killing flagellate
Bibliographic record
Abstract
Abstract Climate change and global warming have led to more frequent harmful algal blooms in the last decade. Among these blooms, Heterosigma akashiwo , a golden‐brown phytoflagellate, is one of the 40 species with a high potential to form harmful blooms, leading to significant fish mortality. Climate change leads to rising atmospheric and ocean temperatures. These changes, along with altered rainfall patterns and meltwater input, can cause fluctuations in ocean salinity. Elevated atmospheric carbon dioxide (CO 2 ) levels increase water acidity as oceans absorb CO 2 . This study investigated the effects of temperature, salinity, and CO 2 levels on lipid production, hemolytic activity, and toxicity of H. akashiwo using the design of experiment approach, which can be used to investigate the effect of two or more factors on the same response simultaneously in a precise manner with fewer experiments and materials but in a larger region of the factor space. The lipid content was measured using a high‐throughput Nile Red method, and the highest level of lipid content was detected at 25°C, a salinity of 30, and a CO 2 concentration of 400 ppm. Hemolytic activity was assessed using rabbit blood erythrocytes in a 96‐well plate, and the optimal conditions for achieving the highest hemolytic activity were determined at 15°C, a salinity of 10, and a CO 2 concentration of 400 ppm. As the chemical structure of the toxin is not known, we used the toxicity against the cell line RTgill‐W1 as the cell toxicity proxy. The maximum toxicity was identified at 15°C, a salinity of 10, and a CO 2 level of 700 ppm.
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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 | 0.001 | 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".