Subcellular Partitioning of Trace Elements Is Related to Metal Ecotoxicological Classes in Livers of Fish (Esox lucius; Coregonus clupeaformis) from the Yellowknife Area (Northwest Territories, Canada)
Bibliographic record
Abstract
The subcellular partitioning of trace elements (TEs) may depend on their binding preferences, although few field data are available from mining-impacted areas. Northern pike and lake whitefish were collected from different aquatic systems located in the Yellowknife mining area (Northwest Territories, Canada) to examine the subcellular partitioning of TEs in liver cells. Elements belonging to metal classes based on binding affinities were considered: A (Ce, La), borderline (As, Pb), and class B (Ag, Cd). Measurements in the metal-detoxified fractions (granule-like structures and heat-stable proteins and peptides) and in the putative metal-sensitive fractions (heat-denatured proteins, mitochondria and microsomes, and lysosomes) revealed marked differences among metal classes. In both fish species, Cd and Ag accumulated more as detoxified forms (higher than 50%, likely bound to metallothionein-like proteins) than La and Ce (not more than 20%). The two borderline TEs (As and Pb) showed an intermediate behavior between classes A and B. Similar proportions were found in the "sensitive" subcellular fractions for all TEs, where quantitative ion character-activity relationships (QICARs) indicated the covalent index and electronegativity as predictors of the TE contribution in this compartment. This study supports the use of classes of metals to predict the toxicological risk of data-poor metals in mining areas.
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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 | 0.001 |
| 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".