A predictive quantitative structure-activity relationship model for the photoinduced toxicity of polycyclic aromatic hydrocarbons to<i>Daphnia magna</i>with the use of factors for photosensitization and photomodification
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants that readily absorb environmentally relevant solar ultraviolet radiation. On absorption of a photon, photoinduced toxicity of PAHs is manifested through photosensitization and photomodification. Both of these processes occur under environmentally relevant levels of actinic radiation. An empirical quantitative structure-activity relationship model previously developed was explanatory of photoinduced toxicity of 16 PAHs in Lemna gibba (duckweed). This model was found to be predictive of toxicity to Vibrio fischeri. The L. gibba quantitative structure-activity relationship showed that a photosensitization factor and a photomodification factor could be combined to describe photoinduced toxicity. To further examine this model, we assessed whether it could be applied to Daphnia magna (water flea), a key bioindicator species in aquatic ecosystems. Toxicity was assessed as median effective concentration and median effective time for immobility. As with L. gibba and V. fischeri, neither the photosensitization factor nor the photomodification factor alone correlated to toxicity in D. magna. However, a photosensitization factor modified for D. magna exhibited a correlation to toxicity (r2 = 0.86), which was modestly improved when summed with a modified photomodification factor (r2 = 0.92). The greatest correlation was observed with median effective concentration data. This research provides evidence that models incorporating factors for photosensitization and photomodification have interspecies applicability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".