Biomonitoring and assessing total mercury concentrations and pools in forested areas
Bibliographic record
Abstract
Abstract: This article focusses on the bio-monitoring of total Hg (THg), sulfur (TS) and carbon (TC) concentrations and pool sizes in forest vegetation and soil layers within the context of a maritime-to-inland transect study in southwestern New Brunswick. This transect stretches from the Grand Manan Island in the Bay of Fundy to the mainland coast (Little Lepreau to New River Beach) and 100 km northward to Fredericton. Along the Bay, frequent summer fogs are thought to have led to increased THg concentrations in forest vegetation and soils such that island THg > coast THg > inland THg concentrations. Transect sampling was done in two phases: (i) a general vegetation and soil survey, and (ii) focusing on specific soil layers (forest floor, top portion of the mineral soils), and select moss and mushrooms species. By way of multiple regression, it was found that soil, moss and mushroom THg and TS were strongly related to one another, with THg decreasing from the island to the inland locations. The accumulated Hg pool within the mineral soil, however, far exceeded (i) the estimated THg pools of the forest biomass (trees, moss and mushrooms) and the forest floor, and (ii) the literature-reported and case-study inferred net input/output rates for annual atmospheric Hg deposition and sequestration, Hg volatilization, and Hg leaching. Partitioning the total soil Hg pool into geogenically and atmospherically derived portions suggested that mineral soils in temperate to boreal forest regions have accumulated and retained atmospherically derived Hg over thousand years and more. These results are summarized in terms of further guiding forest THg monitoring and modelling efforts in terms of specific vegetation and soil sampling targets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".