Role of biogeochemical processes in metal cycling in remote lakes from the Huntsville and Clyde Forks areas, Ontario, and Kejimkujik Park, Nova Scotia, Canada.
Bibliographic record
Abstract
The study of metal speciation and the role of biogeochemical processes in metal distribution and cycling in lake sediments was carried out in remote lakes from the Huntsville and the Clyde Forks areas, Ontario and Kejimkujik Park, Nova Scotia, Canada. Based on geochemical data and sequential extraction results of trace elements from sediment cores of 20 lakes from the Huntsville region, Ontario, Canada, it was demonstrated that relative affinities to organic matter and mineral fractions (silicates) played an important role in the distribution of total metal values and hence in the shape of the metal concentration profiles. The importance of atmospheric and geological trace metal fluxes was studied by using sediment traps in two circum-neutral remote lakes, Lavant Long Lake and Perch Lake, in Lanark County, southeastern Ontario, Canada, to measure sedimentation rates, to quantify trace element fluxes from watershed and atmospheric sources, and to investigate the distribution of elements amongst component phases of the trapped sediments. The results of this study showed that local watershed sources were the dominant source of Hg, Cu, Al, Fe, Mn, Sb, in both lakes with ratio sometimes exceeding 9:1 (geogenic to atmospheric). The study of the effect of humic substances (HS) content and their transformation on their interaction with metals was carried out in three remote lakes: Lavant Long Lake, Perch Lake; and Big Dam Lake. HS transformation was demonstrated by the increase of HA aromaticity and the decrease of the E4/E6 ratio in Lavant Long Lake, by the increase of 13C resonance signal in the aromatic-C region (105--150 ppm) in Perch Lake, and by the gradual decrease of the HS content with depth in Big Dam Lake. A quantitative study of trace metal cycling between pore waters and sediments using the most comprehensive diagenetic models was carried out using a two-layer diagenesis model to account for the bioturbation zone and the zone below the bioturbation zone. Sulfate reducing bacteria (SRB) populations were determined in the three lakes using the most probable number (MPN) method. In the zone of bioturbation of Perch Lake, Co, Cu, and Hg predicted and observed concentrations in the pore waters were close. In Lavant Long Lake and for the rest of the elements in Perch Lake, trace metal concentrations predicted by the model were generally lower than the observed. (Abstract shortened by UMI.)
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".