PCB Concentrations in Lake Trout (<i>Salvelinus namaycush</i>) Are Correlated to Habitat Use and Lake Characteristics
Bibliographic record
Abstract
This study considers the importance of lake trout habitat as a factor determining persistent organochlorine (OC) concentration. Lake trout is a stenothermal, cold water species and sensitive to hypoxia. Thus, factors such as lake depth, thermal stratification, and phosphorus enrichment may determine not only which lakes can support lake trout but may also influence among-lake variability in lake trout population characteristics including bioaccumulation of OCs. A survey of 23 lakes spanning much of the natural latitudinal distribution of lake trout provided a range of lake trout habitat to test the hypothesis that lake trout with greater access to littoral habitat for feeding will have lower concentrations of OCs than lake trout that are more restricted to pelagic habitat. Using the delta13C stable isotope signature in lake trout as an indicator of influence of benthic littoral feeding, we found a negative correlation between lipid-corrected delta13C and sigmaPCB concentrations supporting the hypothesis that increasing accessto littoral habitat results in lower OCs in lake trout. The prominence of mixotrophic phytoplankton in lakes with more contaminated lake trout indicated the pelagic microbial food web may exacerbate the biomagnification of OCs when lake trout are restricted to pelagic feeding. A model that predicted sigmaPCB in lake trout based on lake area and latitude (used as proximate variables for proportion of littoral versus pelagic habitat and accessibility to littoral habitat respectively) explained 73% of the variability in sigmaPCBs in lake trout in the 23 lakes surveyed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".