Trophic magnification factors: Considerations of ecology, ecosystems, and study design
Bibliographic record
Abstract
Recent reviews by researchers from academia, industry, and government have revealed that the criteria used by the Stockholm Convention on persistent organic pollutants under the United Nations Environment Programme are not always able to identify the actual bioaccumulative capacity of some substances, by use of chemical properties such as the octanol-water partitioning coefficient. Trophic magnification factors (TMFs) were suggested as a more reliable tool for bioaccumulation assessment of chemicals that have been in commerce long enough to be quantitatively measured in environmental samples. TMFs are increasingly used to quantify biomagnification and represent the average diet-to-consumer transfer of a chemical through food webs. They differ from biomagnification factors, which apply to individual species and can be highly variable between predator-prey combinations. The TMF is calculated from the slope of a regression between the chemical concentration and trophic level of organisms in the food web. The trophic level can be determined from stable N isotope ratios (δ(15) N). In this article, we give the background for the development of TMFs, identify and discuss impacts of ecosystem and ecological variables on their values, and discuss challenges and uncertainties associated with contaminant measurements and the use of δ(15) N for trophic level estimations. Recommendations are provided for experimental design, data treatment, and statistical analyses, including advice for users on reporting and interpreting TMF data. Interspecies intrinsic ecological and organismal properties such as thermoregulation, reproductive status, migration, and age, particularly among species at higher trophic levels with high contaminant concentrations, can influence the TMF (i.e., regression slope). Following recommendations herein for study design, empirical TMFs are likely to be useful for understanding the food web biomagnification potential of chemicals, where the target is to definitively identify if chemicals biomagnify (i.e., TMF > or < 1). TMFs may be less useful in species- and site-specific risk assessments, where the goal is to predict absolute contaminant concentrations in organisms in relation to threshold levels.
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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 | 0.260 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".