Effects of Experimental Thermocline and Oxycline Deepening on Methylmercury Bioaccumulation in a Canadian Shield Lake
Bibliographic record
Abstract
Environmental disturbances like deforestation or climate change may influence lake thermal and oxic stratification, thereby modifying cycles of contaminants such as mercury (Hg). In a lake naturally separated into three basins, the thermocline and oxycline of an experimental basin were deepened by 4 and 3 m, respectively, to study the effect on the methylmercury (MeHg) accumulation. This treatment decreased hypolimnetic MeHg concentration by approximately 90%, zooplankton concentrations by 30 to 50%, and in some fish by 45%. A multiple linear regression indicated that oxycline depth significantly influenced hypolimnetic MeHg concentrations, with no significant effect of thermocline depth, anoxic water volume, interface area of oxic-anoxic water, and sediment area in contact with anoxic water. Fish MeHg decline varied, with a greater response by low oxygen-tolerant bullhead. Increased pelagic primary and secondary production likely caused zooplankton and fish MeHg decreases via algal and growth dilution. Environmental changes leading to oxycline deepening are therefore predicted to cause a decrease in MeHg bioaccumulation in similar Canadian Shield lakes. If associated ecosystem impacts related to the deepening treatment are deemed acceptable, then this experiment provides a potential remediation method for small lakes confronted with MeHg accumulation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".