Review of existing terrestrial bioaccumulation models and terrestrial bioaccumulation modeling needs for organic chemicals
Bibliographic record
Abstract
Abstract Protocols for terrestrial bioaccumulation assessments are far less-developed than for aquatic systems. This article reviews modeling approaches that can be used to assess the terrestrial bioaccumulation potential of commercial organic chemicals. Models exist for plant, invertebrate, mammal, and avian species and for entire terrestrial food webs, including some that consider spatial factors. Limitations and gaps in terrestrial bioaccumulation modeling include the lack of QSARs for biotransformation and dietary assimilation efficiencies for terrestrial species; the lack of models and QSARs for important terrestrial species such as insects, amphibians and reptiles; the lack of standardized testing protocols for plants with limited development of plant models; and the limited chemical domain of existing bioaccumulation models and QSARs (e.g., primarily applicable to nonionic organic chemicals). There is an urgent need for high-quality field data sets for validating models and assessing their performance. There is a need to improve coordination among laboratory, field, and modeling efforts on bioaccumulative substances in order to improve the state of the science for challenging substances. Integr Environ Assess Manag 2016;12:123–134. © 2015 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC. Key Points The report reviews models available for assessing the bioaccumulation potential of organic compounds in terrestrial food webs. Major limitations in terrestrial bioaccumulation modeling include the lack of QSARs for biotransformation and dietary assimilation efficiencies for terrestrial species, and the lack of models and QSARs for important terrestrial species such as insects, amphibians and reptiles. Other limitations include the limited chemical domain of existing bioaccumulation models and QSARs, and the lack of standardized testing protocols for plants that has limited development of plant models.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".