A Generic QSAR for Assessing the Bioaccumulation Potential of Organic Chemicals in Aquatic Food Webs
Bibliographic record
Abstract
Abstract This study presents the development of a quantitative‐structure activity relationship (QSAR) for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. The QSAR is derived by parameterization and calibration of a mechanistic food web bioaccumulation model. Calibration of the QSAR is based on the derivation of a large database of bioconcentration and bioaccumulation factors, which is evaluated for data quality. The QSAR provides estimates of the bioaccumulation potential of organic chemicals in higher trophic level fish species of aquatic food webs. The QSAR can be adapted to include the effect of metabolic transformation and trophic dilution on the BAF. The BAF‐QSAR can be applied to categorize organic chemical substances on their bioaccumulation potential. It identifies chemicals with a log K OW between 4.0 and 12.2 to exhibit BAFs greater than 5 000 in the absence of significant metabolic transformation rates. The BAF‐QSAR can also be used in the derivation of water quality guidelines and total maximum daily loadings by relating internal concentrations of organic chemicals in upper trophic fish species to corresponding concentrations in the water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".