Modeling cadmium uptake from water and food by the freshwater bivalve <i>Pyganodon grandis</i>
Bibliographic record
Abstract
For this study, the unionid bivalve Pyganodon grandis was either exposed to dissolved cadmium (Cd) or fed Cd-contaminated algae ( Pseudokirchneriella subcapitata ) during short-term laboratory experiments. Cadmium accumulated largely in the digestive gland after a dietary exposure, or in the gills after an aqueous exposure; in these latter experiments, Cd accumulation from the dissolved phase increased as bivalve filtration rates increased. The results of these uptake experiments were used to parameterize a biodynamic model, which was then used to estimate the relative importance of water and food as sources of Cd for this bivalve, and to predict steady state Cd concentrations in the gills and digestive gland of native bivalves. In comparisons between the simulations and data obtained from earlier field studies on P. grandis, the model adequately predicted Cd concentrations in P. grandis gills, except in Ca-rich lakes, whereas it tended to overestimate Cd concentrations in the digestive gland. The field simulations indicate that water is the main source of Cd for both the gills (relative importance water:food::99:1) and the digestive gland (water:food::80:20). These results will facilitate the interpretation of spatial and temporal variations in Cd concentrations in free-living P. grandis, which is a promising metal biomonitor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".