Model-based exploration of the variability in lake trout (<i>Salvelinus namaycush</i>) bioaccumulation factors: The influence of physiology and trophic relationships
Bibliographic record
Abstract
Abstract Because dietary consumption of fish is often a major vector of human exposure to persistent organic pollutants (POPs), much effort is directed toward a quantitative understanding of fish bioaccumulation using mechanistic models. However, many such models fail to explicitly consider how uptake and loss rate constants relate to fish physiology. We calculated the bioaccumulation factors (BAFs) of hypothetical POPs, with octanol–water partition coefficients ranging from 104.5 to 108.5, in lake trout (Salvelinus namaycush) with a food-web bioaccumulation model that uses bioenergetics to ensure that physiological parameters applied to a species are internally consistent. We modeled fish in 6 Canadian lakes (Great Slave Lake, Lake Ontario, Source Lake, Happy Isle Lake, Lake Opeongo, and Lake Memphremagog) to identify the factors that cause the BAFs of differently sized lake trout to vary between and within lakes. When comparing differently sized lake trout within a lake, larger fish tend to have the highest BAF because they allocate less energy toward growth than smaller fish and have higher activity levels. When comparing fish from different lakes, the model finds that diet composition and prey energy density become important in determining the BAF, in addition to activity and the amount of total energy allocated to growth. Environ Toxicol Chem 2019;38:831–840. © 2019 SETAC Abstract A bioaccumulation model accounting for bioenergetic constraints on the rates of food consumption, respiration, and growth is used to explain differences in the bioaccumulation factor of persistent organic contaminants between differently sized trout in the same lake and between similarly sized trout from different lakes. BAF = bioaccumulation factor; KOW = octanol–water partitioning coefficient. Source: Knepp, Timothy, US Fish and Wildlife Service, public domain, https://commons.wikimedia.org/wiki/File:Lake_trout_fishes_salvelinus_namaycush.jpg
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".