Quantitative Structure Activity Relationships for Predicting the Bioaccumulation of POPs in Terrestrial Food‐Webs
Bibliographic record
Abstract
Abstract KOW based QSARs are used to assess the bioaccumulation potential of thousands of commercial chemicals in Canada and internationally. The QSARs, which are based on information from aquatic organisms, identify chemicals with a log KOW>5 to have a potential to biomagnify in food‐chains. This study investigates whether KOW based QSARs are also effective in identifying biomagnifying chemicals in terrestrial food‐chains. First, a terrestrial bioaccumulation model is developed and used to hypothesize the general relationship between the chemical's octanol‐air and octanol‐water partition and its biomagnification potential. Secondly, field observations of the bioaccumulation of persistent organic pollutants in wolves are used to test the hypothesis and explore the fundamental differences between QSARs for bioaccumulation in aquatic and terrestrial food‐chains. The results indicate that (i) QSARs for bioaccumulation in terrestrial food‐chains should include both octanol‐air (KOA) and octanol water partition coefficients (KOW); (ii) chemicals with a log KOA>approximately 5 can biomagnify in terrestrial food‐chains if log KOW>2 and the rate chemical transformation or metabolism is low; (iii) biomagnification factors in terrestrial food‐chains are much greater than those in aquatic food‐chains; (iv) biomagnification factors of very hydrophobic substances (log KOW>7) in terrestrial biota do not drop off with increasing KOW as has been observed in aquatic biota. The relevance of these findings is that current regulations and protocols may misidentify (i) low KOW but high KOA chemicals as having no bioaccumulation potential and (ii) very hydrophobic (log KOW>8.5) which appear not to biomagnify in aquatic organisms but have the potential to biomagnify in terrestrial food‐chains. Considering that 67.9% of the approximately 12 000 organic chemicals on Canada's Domestic Substances List exhibit high KOA but low KOW, this represents a major gap in our methods for screening bioaccumulative substances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".