Isotopic and chemical characteristics of mercury in organs and tissues of fish in a mercury-polluted lake: Evidence for fractionation of mercury isotopes by physiological processes
Bibliographic record
Abstract
Abstract Organs and tissues of whitefish and trout from mercury (Hg)-polluted Lake Ontario were analyzed for Hg isotopes, methylmercury (CH3Hg+), and inorganic Hg to investigate possible mass-dependent fractionation (MDF) and mass-independent fractionation (MIF) of Hg isotopes by physiological processes of the fish. Isotope signatures of different body parts were defined by δ-values of 198Hg/202Hg, 199Hg/202Hg, 200Hg/202Hg, and 201Hg/202Hg ratios and by Δ-values representing effects of MIF on 199Hg/202Hg and 201Hg/202Hg ratios. The research yielded the following evidence for MDF and MIF, including MIF of isotopes with even as well as odd mass numbers, by metabolic activities: 1) anomalously low δ-values for whitefish kidneys but not for trout kidneys; 2) widely varying differences between the δ-values of different body parts of whitefish but practically uniform differences for those of trout; 3) different relationships between Δ199Hg and Δ201Hg for whitefish than for trout; 4) nonlinear correlation between δ198Hg and δ200Hg for whitefish but linear correlation for trout; 5) an inverse correlation between the δ199Hg values and CH3Hg+ concentrations of whitefish and trout; 6) an inverse correlation between the δ201Hg/δ199Hg and CH3Hg+/inorganic Hg ratios of trout kidneys and gills (and lipids of trout near the sources of pollution) but a positive correlation for muscle, liver, and gut; and 7) inverse correlations between Δ199Hg and the CH3Hg+/inorganic Hg ratio for trout liver, kidneys, and gut. Environ Toxicol Chem 2018;37:515–529. © 2017 SETAC
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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) | 0.001 | 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".