The effect of multiple environmental stressors on the growth and toxicity of the red tide alga Heterosigma akashiwo
Bibliographic record
Abstract
Heterosigma akashiwo (Y.Hada) Y.Hada ex Y.Hara & M.Chihara is a golden-brown phytoflagellate with high potential to kill fish. These cells create large, nearly mono-specific blooms that persist from weeks to months. Although bloom persistence and frequency remain a mystery, environmental factors such as light, temperature, salinity and CO2 level are proposed as drivers for both bloom initiation and toxicity. As timing and locations of nature blooms are difficult to predict, most of the information on this species comes from laboratory experiments on isolated cells. In this age, when multiple stressors occur simultaneously the traditional “One-factor-at-a-time” (OFAT) approach limits our understanding of how the cells respond to environmental change. Here, I consider the simultaneous effect of multiple parameters and their interaction by employing a design-of-experiment (DOE) approach. The results suggested that the DOE approach is an appropriate method to determine the impact of multi-environmental factors on both bloom formation and toxicity of H. akashiwo.\nSimilarly, the measurement of “fish killing” activities requires the use of an experimental proxy when cells are grown in the laboratory. There is a critical need to understand toxicity in “fish-kill” species. Two commonly employed assays, the rainbow trout cell line RTgill-W1 cytotoxicity assay (RCA) and the erythrocyte lysis assay (ELA) were evaluated against combinatorial environmental conditions (temperature, pCO2, salinity). Increased temperature and pCO2 reduced the expression of toxicicty based on these two assays.\nWith regards to the future conditions of warmer temperatures, and elevated levels of CO2, which impact the salinity, water temperature, and the absorbance of CO2, in many coastal regions worldwide, it is expected these abiotic changes will likely increase the potential growth rate and biomass yield but reduce the toxicity of fish-killing flagellate H. akashiwo in North America.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".