Uptake and Subcellular Distributions of Cadmium and Selenium in Transplanted Aquatic Insect Larvae
Bibliographic record
Abstract
We transplanted larvae of the phantom midge Chaoborus punctipennis from a lake having lower concentrations of Cd and Se (Lake Dasserat) to a more contaminated lake (Lake Dufault) located near a metal smelter in Rouyn-Noranda, Quebec. Transplanted individuals were held in mesh mesocosms for up to 16 days where they were fed with indigenous contaminated zooplankton. Larval Cd and Se burdens increased over time, and came to equal those measured in indigenous C. punctipennis from contaminated Lake Dufault. Larval Se burdens increased steadily, whereas those of Cd showed an initial lag phase that we explain by a change in the efficiency with which this insect assimilated Cd from its prey. We measured Cd and Se in subcellular fractions and found that larvae sequestered the majority (60%) of the incoming Cd in a detoxified fraction containing metal-binding proteins, whereas a minority of this nonessential metal was in sensitive fractions (20%). In contrast, a much higher proportion of the essential element Se (40%) was apportioned to metabolically active sensitive fractions. Larvae took up equimolar quantities of these elements over the course of the experiment. Likewise, Cd and Se concentrations in wild larvae were equimolar, which suggests that they are exposed to equimolar bioavailable concentrations of these elements in our study lakes.
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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".