Mapping differential elemental accumulation in fish tissues: Importance of fish tissue sampling standardization
Bibliographic record
Abstract
The concentrations of As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se and Zn in the muscle, gills, liver and intestine of the wels catfish (Silurus glanis) from the Danube River were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The aim of the study was to determine whether in complex muscle/skin, gill filament/gill arch, proximal/distal liver and proximal/median/distal intestine samples, particular components differ in concentrations of the analyzed elements. Results indicated that there were no differences in the accumulation of different elements between the proximal and distal liver segments and between the proximal and median intestine sections. Conversely, elemental accumulation patterns in muscle and skin differed significantly. Significant differences were also observed between the gill arch and filaments, as well as between the distal and the two upper intestine sections. Findings indicated the importance of detailed reporting of tissue sampling, i.e. whether the skin was included in the muscle sample, as well as if the gill arch and filaments were analyzed together. Due to a potential bias that can be produced by different muscle/skin or gill arch/filament ratios included in the sample, we strongly recommend that they should not be analyzed together. Results of the present study might be of interest to the scientific community and stakeholders involved in aquatic ecosystem monitoring programs.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".