Luminescence Facilitated Detection of Bioavailable Mercury in Natural Waters
Bibliographic record
Abstract
One of the major routes of human exposure to mercury is by the consumptron of contammated fish and shellfish. Mercury, in the form of methyl mercury (MM), accumulates in these biota by biomagnification through the aquatic food chain, to concentrations orders of magnitude higher than its levels in the water ( 1 , 2 ). Dissolved MM is absorbed by unicellular organisms ( 2 , 3 ) at the base of the food chain, and since MM is only very slowly eliminated from the animal body, its concentration increases with the trophic level. The amount of dissolved MM available to the base of the food chain is critical, and this amount is determined by the rates of MM formation and degradation and by factors that directly and indirectly affect these rates. Thus, the concentratton of bioavailable ionic mercury (Hg 2+ ) affects not only the methylation rate, but also the rate of the Hg 2+ reduction and volatilization, reactions that compete with methylation for the same substrate ( 4 ). Furthermore, Hg 2+ is the inducer of a bacterial enzyme, organomercurial lyase, that degrades MM, as well as the reduction process ( 5 ). Measuring bioavailable Hg 2+ is essential for calculating methylation and reduction rates in situ , a measurement needed for evaluating the potential for MM accumulation and thus risk to public health. Total mercury levels presently serve as the basis for regulating mercury exposure. Because the majority of mercury in the environment is in a harmless inert form, accurate measurements of bioavailable Hg 2+ may provide a basis for more realistic regulatory criteria. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".