The role of melano-macrophage aggregates in the storage of mercury and other metals: An example from yelloweye rockfish (<i>Sebastes ruberrimus</i>)
Bibliographic record
Abstract
Melano-macrophage aggregates, collections of specialized cells of the innate immune system of fish, are considered a general biomarker for contaminant toxicity. To elucidate further the relationship between macrophage aggregates and metals exposure, yelloweye rockfish (Sebastes ruberrimus), a long-lived species, were sampled from the east and west coasts of Prince of Wales Island, Alaska. Metals concentrations in livers (inorganic Hg, methyl mercury, Se, Ni, Cd, Cu, Zn) and spleens (inorganic Hg and methyl mercury) were determined, as well as their correlations with melano-macrophage aggregate area. Sections of liver tissue were analyzed by laser ablation-inductively coupled plasma-mass spectrometry to determine how metals were spatially distributed between hepatocytes and macrophage aggregates. The concentration of inorganic Hg in whole tissue was the best predictor of macrophage area in yelloweye livers and spleens. Macrophage aggregates had higher relative concentrations than most metals compared with the surrounding hepatocytes. However, not all metals were accumulated to the same degree, as evidenced by differences in the ratios of metals in macrophages compared with hepatocytes. Laser ablation data were corroborated with the results of X-ray synchrotron fluorescence imaging of a yelloweye liver section. Hepatic macrophage aggregates in yelloweye rockfish may play an important role in the detoxification and storage of Hg and other metals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".