Dissolved Organic Carbon Thresholds Affect Mercury Bioaccumulation in Arctic Lakes
Bibliographic record
Abstract
Dissolved organic carbon (DOC) is known to affect the Hg cycle in aquatic environments due to its overriding influence on complexation, photochemical, and microbial processes, but its role as a mediating factor in the bioaccumulation of Hg in aquatic biota has remained enigmatic. Here, we examined 26 tundra lakes in Canada's western Arctic that span a large gradient of DOC concentrations to show that total Hg (HgT) and methyl mercury (MeHg) accumulation by aquatic invertebrates is defined by a threshold response to Hg-DOC binding. Our results showed that DOC promotes HgT and MeHg bioaccumulation in tundra lakes having low DOC (<8.6 - 8.8 mg C L(-1); DOC threshold concentration, TC) whereas DOC inhibits HgT and MeHg bioaccumulation in lakes having high DOC (>DOC TC), consistent with bioaccumulation results in a companion paper (this issue) using a microbial bioreporter. Chemical equilibrium modeling showed that Hg bioaccumulation factors were elevated when Hg was associated mainly to fulvic acids, but became dramatically reduced when DOC was >8.5 mg C L(-1), at which point Hg was associated primarily with strong binding sites on larger, less bioaccessible humic acids. This study demonstrates that the biological uptake of Hg in lakes is determined by binding thresholds on DOC, a water quality variable predicted to change markedly with future environmental change.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".