Organic carbon, mercury and climate change: towards a better understanding of biotic contamination in the Canadian Arctic
Bibliographic record
Abstract
Mercury (Hg) is a known neurotoxin that is often found in concentrations exceeding safe consumption guidelines in aquatic biota. This is evident in northern Canada, where northerners consume significant amounts of animals such as beluga, seals and burbot. In the Mackenzie River Basin, recent increases in Hg concentration in many of these animals over the past 25 years have been observed. The warming climate, and with it, the changing carbon cycle, are hypothesised in this thesis to play a role in the increases. Within the context of the two major zones (mountainous and peatland), with distinct geomorphology, hydrology and geology, traditional fossil fuel exploration methods (Rock-Eval pyrolysis, organic petrography) have been employed in a novel manner on recent sediments to qualify and quantify the OM and several geochemical analyses have been used to determine the geochemical sources of Hg. The mountainous zone is composed mostly of refractory OM, from forest fire char and heavily reworked OM. It also contains, and fluxes, most of the Hg, which derives from oxidative weathering and erosion of widespread sulfide minerals. However, Hg from this zone is in chemical forms of limited bioavailability. The peatland zone has a greater proportion of labile OM, with higher concentrations of DOC and algal-derived OM. Lake-fed tributaries in this zone contain even higher proportions of labile OM. At one of these sites, the sediment core record shows that Hg has been increasingly associated with labile OM over time, due to increasing primary productivity accelerated by climate change, and is resulting in an increase in scavenged Hg. The temporal trend in algal-bound Hg in the sediment record matches very well with the temporal trend of Hg in burbot sampled from the area, providing one of the first and strongest lines of evidence for the climatic impact on Hg bioaccumulation in Arctic ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".