A little bit of light goes a long way: the role of phototrophs on mercury cycling
Bibliographic record
Abstract
Among toxic metals, mercury (Hg) is a global priority contaminant due to the biomagnification of the most toxic form methylmercury (MeHg) in food webs, even in remote regions, such as the high Arctic. The importance of Hg as a chemical of major concern to human health was underscored by the recent adoption of the Minamata Convention on Mercury, a legally binding treaty that requires government agencies be equipped to monitor processes affecting global mercury transport and cycling. For several decades now, field and laboratory experiments have shown that phototrophs can directly interact with Hg and affect its speciation and fate. While an important body of work on the role of chemotrophic microbes on Hg cycling has been undertaken, the role of phototrophs is too often overlooked. Strikingly, what is known about phototroph-Hg interactions pertains mostly to oxygenic phototrophs with relatively little being known about anoxygenic phototrophs. Ongoing environmental change will no doubt affect the physical and chemical properties of aquatic ecosystems, which in turn will alter all phototrophic community dynamics. How these changes will affect the Hg cycle represent an important knowledge gap. After synthesizing what is currently known about chemotrophic Hg transformations, we describe the current state of knowledge on what is known about how phototrophs (bacteria and algae) affect Hg cycling (i.e., alteration of Hg redox state, Hg scavenging, potential for methylation) as well as describe the cellular and molecular targets of Hg toxicity in phototrophs. We discuss these interactions in an evolutionary context and provide recommendations for future research directions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".