Trophic magnification factors of volatile methylsiloxanes measured and predicted in freshwater and marine environments
Bibliographic record
Abstract
The trophic magnification factor (TMF) is an important metric for evaluating chemical biomagnification in food webs. However, reported TMF values of cyclic volatile methylsiloxanes (VMS) vary widely, presumably due to the spatial gradient of chemical concentrations and sampling biases. This study surveyed biota and sediment concentrations of cyclic VMS and two reference polychlorinated biphenyls (PCBs; PCB-153 and PCB-180) in the rocky and sandy areas of the Yugawara coast, Japan. Biota concentrations and TMFs were also predicted by the Multibox-AQUAWEB model for the food webs in the same areas. The predicted biota concentrations and TMFs of the cyclic VMS and PCBs were in good agreement with the measured values. In the rocky and sandy areas, the mean TMFs of cyclic VMS were <1 with strong or moderate statistical significance, suggesting trophic dilution, while the mean TMFs of the PCBs exceeded 1 with strong statistical significance, indicating trophic magnification. The Multibox-AQUAWEB model was applied to predict TMFs for three cyclic VMS and five linear VMS in six global aquatic food webs: Lake Erie, False Creek, Lake Pepin, Lake Ontario, Inner Oslofjord, and Tokyo Bay. Predicted TMFs ranged from 0.13 to 1.00 for all VMS-food web pairs, except for L5 in Lake Pepin (TMF = 1.10; 95th percentile confidence interval [0.75, 1.61]), lacking statistical significance (p > 0.05). It is noted that none showed TMFs of VMS >1 with statistical significance. Thus, it is unlikely that VMS would be trophically magnified in aquatic food webs. To improve model predictions, more precise measurements of dietary uptake efficiencies and somatic biotransformation rate constants of VMS are needed, as trends against molecular weight or logKOW were not clearly demonstrated.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".