Evaluating the roles of biotransformation, spatial concentration differences, organism home range, and field sampling design on trophic magnification factors
Bibliographic record
Abstract
Trophic magnification factors (TMFs) are field-based measurements of the bioaccumulation behavior of chemicals in food-webs. TMFs can provide valuable insights into the bioaccumulation behavior of chemicals. However, bioaccumulation metrics such as TMF may be subject to considerable uncertainty as a consequence of systematic bias and the influence of confounding variables. This study seeks to investigate the role of systematic bias resulting from spatially-variable concentrations in water and sediments and biotransformation rates on the determination of TMF. For this purpose, a multibox food-web bioaccumulation model was developed to account for spatial concentration differences and movement of organisms on chemical concentrations in aquatic biota and TMFs. Model calculated and reported field TMFs showed good agreement for persistent polychlorinated biphenyl (PCB) congeners and biotransformable phthalate esters (PEs) in a marine aquatic food-web. Model testing showed no systematic bias and good precision in the estimation of the TMF for PCB congeners but an apparent underestimation of model calculated TMFs, relative to reported field TMFs, for PEs. A model sensitivity analysis showed that sampling designs that ignore the presence of concentration gradients may cause systematically biased and misleading TMF values. The model demonstrates that field TMFs are most sensitive to concentration gradients and species migration patterns for substances that are subject to a low degree of biomagnification or trophic dilution. The model is useful in anticipating the effect of spatial concentration gradients on the determination of the TMF; guiding species collection strategies in TMF studies; and interpretation of the results of field bioaccumulation studies in study locations where spatial differences in chemical concentration exist.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".