Mercury and Organic Matter Concentrations in Lake and Stream Sediments in relation to One Another and to Atmospheric Mercury Deposition and Climate Variations across Canada
Bibliographic record
Abstract
This article focuses on analyzing the Geological Survey of Canada (GSC) data for total mercury concentrations (THg) in lake and stream sediments. The objective was to quantify how sediment THg varies by (i) sediment organic matter, determined by loss on ignition (LOI) at 500∘C, (ii) atmospheric Hg deposition (atm. Hgdep ) as derived from the Global/Regional Atmospheric Heavy Metals Model GRAHM2005, and (iii) mean annual precipitation and mean monthly July and January temperatures ( TJuly , TJan ). Through regression analyses and averaging by National Topographic System tiles (NTS, 1:250,000 scale), it was found that 40, 70, and 80% of the sediment THg, LOI, and atm. Hgdep variations were, respectively, related to precipitation, TJuly , and TJan . In detail, lake sediment THg was related to atm. Hgdep and precipitation, while stream sediment THg was related to sediment LOI and TJuly . Plotting sediment THg versus sediment LOI revealed a curvilinear pattern, with highest Hg concentrations at intermediate LOI values. Analysing the resulting 10th and 90th log10THg percentiles within each 10% LOI class from 0 to 100% revealed that (i) atm. Hgdep contributed to the organic component of sediment THg and (ii) this was more pronounced for lakes than for streams.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".