Sub-cellular partitioning of essential and non-essential metals in a freshwater mollusc,<i>Pyganodon grandis</i>, collected in the field along a polymetallic environmental gradient
Bibliographic record
Abstract
The cellular alterations normally induced by metals at high concentrations can be prevented by detoxification processes [1] such as sequestration into cellular compartments (calcium concretions, lysosoiues, etc.) or their binding to specific cellular ligands like metallothionein [2]. The aim of this project was to study and compare the subcellular partitioning of three metals (Cd. Cu, Zn) in gills of a freshwater molluse, Pyganodon grandis, collected along a polymetallic environmental gradient (nine lakes in the Rouyn-Noranda area, Abitibi, QC, Canada). Differential centrifugation was used to partition metals among different subcellular fractions. In the gills, along the environmental metal gradient, total tissue metal concentrations were positively correlated with concentrations in the granule traction; gill tissues contained high amounts of calcium concretions, which acted as preferential sites for sequestration of the three metals. An increase in Cd concentration was observed in the heat stable proteins fraction (including metallothionein), but not in the heat-denatured proteins fraction, suggesting that Cd-induced cell injury could be prevented by the involvement of maltiple cellular compartments in a protective role.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".