In situ measurement of tissue turnover and energy conversion efficiencies in lake trout (<i>Salvelinus namaycush</i>) using a novel toxicokinetic approach
Bibliographic record
Abstract
We determined polychlorinated biphenyl (PCB) elimination patterns in lake trout (Salvelinus namaycush) from Lake Ontario using biomonitoring data collected from 1977 to 1993. The in situ elimination rates of these persistent pollutants were found to describe tissue turnover rates in lake trout. A model relating tissue turnover rates and endogenous energy conversion efficiencies revealed that chemical elimination in larger organisms is primarily regulated by food limitation and bioenergetic mechanisms rather than chemical kinetics. Lake trout approximately 2500 g and larger were observed to have higher PCB elimination rates than smaller fish as a result of increased lipid mobilization to supplement metabolic demands due to increased time spent foraging. This study concludes that the growth and production of large predators in Lake Ontario are regulated by the bioenergetic constraints of searching for prey in a food-limited environment. We also demonstrate that persistent organic pollutant kinetics can describe the proportion of endogenous energy required to support metabolism and production, thus providing important in situ measurements of bioenergetic processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".