Quantitative structure-activity relationship for the photoinduced toxicity of polycyclic aromatic hydrocarbons to the luminescent bacteria<i>Vibrio fischeri</i>
Bibliographic record
Abstract
Sunlight can greatly enhance the toxicity of polycyclic aromatic hydrocarbons (PAHs). Photosensitization reactions (e.g., generation of singlet-state oxygen) and photomodification reactions (e.g., photooxidation of PAHs to more toxic species) are both pathways of photoinduced toxicity of PAHs. Previously, a quantitative structure-activity relationship (QSAR) was developed for PAHs showing that a photosensitization factor (PSF) and photomodification factor (PMF) can be additively combined to describe photoinduced toxicity. That QSAR model was developed for the photoinduced toxicity of 16 PAHs to the higher plant Lemna gibba. The objective of this study was to apply the QSAR model developed for L. gibba to another organism. The organism chosen was the luminescent marine bacteria Vibriofischeri. Toxicity data used for the QSAR model were inhibition of luminescence and inhibition of growth of V. fischeri. Both short-term (15 min) and long-term (18 h) assays of toxicity were used. Light did not impact on PAH toxicity in the short-term assay, and thus the QSAR model did not correlate well with these data. Conversely, light greatly enhanced toxicity when the long-term assay was employed. The PMFs for the PAHs from the L. gibba QSAR showed a moderate correlation to bacterial toxicity in the long-term assay, whereas the PSFs showed only a weak correlation to toxicity. As was the case for L gibba, summing the PMF and the PSF resulted in a strong correlation to toxicity that had predictive value. Thus, a QSAR model derived for plants accurately described the toxicity of PAHs to a bacterial species. This indicates that the bipartite mechanism of PAH-photoinduced toxicity may be applicable to other organisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".