An Assessment of Polycyclic Aromatic Hydrocarbons Using Estimation Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the environment, the class of chemicals known as polycyclic aromatic hydrocarbons (PAHs) behave somewhat differently. This review covers situations where PAHs can be ‘labile’ and where they can be persistent. The in-silico prediction of toxicity and the properties of selected 29 PAHs were estimated using programs developed by the U.S. Environmental Protection Agency (EPA), such as the Estimation Programs Interface (E.P.I.) and the Toxicity Estimation Software Tool (version 5.1.2) (TEST), with online software such as SwissADME and SwissDock. TEST was used to estimate the LC50 of the fathead minnow (with a range of 14.53 mg/L for 1-indanone and 2.14 × 10−2 mg/L for cyclopenta[c,d]pyrene), the LC50 of Daphnia magna (with a range of 14.95 mg/L for 1-indanone and 7.53 × 10−2 mg/L for coronene), the IGC50 of Tetrahymena pyriformis (with a range of 66.14 mg/L for 1-indanone and 0.36 mg/L for coronene), the bioconcentration factor (8.36 for 1,2-acenaphthylenedione and 910.1 for coronene), the developmental toxicity (0.30 (−) for 1,2-acenaphthylenedione and 0.82 (+) for 4-hydroxy-9-fluorenone), and the mutagenicity (0.25 (−) for 2-methyl-9-fluorenone and 1.09 (+) for coronene). The carbon chain and molecular weight have a significant effect on the properties of PAHs. Overall, it was found that PAHs with a lower molecular weight (LMW) have a higher water solubility and LC50 value and a smaller LogKow value, whereas the opposite is true for heavier PAHs, with TEST predicting that PAHs with an MW of over 168.2 g/mol, with a few exceptions, are mutagenic. Hence, LMW PAHs have a higher potential to be in the environment but are less toxic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it