Significance of chemical affinity on metal subcellular distribution in yellow perch (Perca flavescens) livers from Lake Saint-Pierre (QUEBEC, Canada)
Bibliographic record
Abstract
The subcellular partitioning approach provides useful information on the location of metals within cells and is often used on organisms with high levels of bioaccumulation to establish relationships between the internal concentration and the potential toxicity of metals. Relatively little is known about the subcellular partitioning of metals in wild fish with low bioaccumulation levels in comparison with those from higher contaminated areas. This study aims to examine the subcellular partitioning of various metals considering their chemical affinity and essentiality at relatively low contamination levels. Class A (Y, Sr), class B (Cu, Cd, MeHg), and borderline (Fe, Mn) metal concentrations were measured in livers and subcellular fractions of yellow perch (n = 21) collected in Lake Saint-Pierre, QC, Canada. The results showed that all metals, apart from MeHg, were distributed among subcellular fractions according to their chemical affinity. More than 60% of Y, Sr, Fe, and Mn were found in the metal-sensitive fractions. Cd and Cu were largely associated with the metallothionein-like proteins and peptides (60% and 67% respectively) whereas MeHg was found mainly in the metal-sensitive fractions (86%). In addition, the difference between the subcellular distribution of Cu and other essential metals like Fe and Mn denotes that, although the essentiality of some metals is a determinant of their subcellular distribution, the chemical affinity of metals is also a key driver. The similarity of the subcellular partitioning results with previous studies on yellow perch and other fish species from higher contaminated areas supports the idea that metals are distributed in the cellular environment according to their chemical properties regardless of the bioaccumulation gradient.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".