MERCURY EXPORT FROM SMALL FORESTED WATERSHEDS IN WESTCENTRAL NOVA SCOTIA, CANADA: DEVELOPMENT OF AN ECOLOGICAL MODEL
Bibliographic record
Abstract
As an efficient filter of airborne Hg compounds, forests retain a significant portion of the Hg deposited to the land. Forested watersheds have been identified as major sources of low-concentration Hg compounds to surrounding streams and lakes. Mercury export from forests is highly variable in both space and time. It is difficult to use field surveys alone to capture the spatiotemporal variation inherent in this variable. Mathematical models are required for improved representation.\nThe objective of this Thesis is to develop and test a monthly dynamic model that can be used to estimate seasonal Hg export from forested watersheds to low-ordered forest streams. The fully developed model consists of four model components: (i) a forest hydrology component, to simulate variation in soil temperature, soil moisture, and stream discharge for input to the other model components; (ii) a forest nutrient cycling and biomass growth component, to simulate forest growth and litter production; (iii) a forest litter decomposition component, to simulate seasonal production of dissolved organic carbon (DOC); and (iv) a monthly DOC and Hg export component to simulate the translocation of DOC and Hg from forested watersheds to low-ordered streams. The Hg-export component incorporates an Hg-to-DOC binding coefficient estimated from a one-time stream survey of Hg and DOC concentrations.\nSimulations of in-stream Hg concentrations show two main trends: (i) an annual trend, associated with the seasonal (monthly) dynamics of forest litter production, decomposition, and DOC production and export, and (ii) a multiple-year trend, associated with forest harvesting and re-growth patterns of regenerating forests. This study demonstrates that (i) wetland- and conifer-dominated watersheds release a greater amount of Hg to aquatic ecosystems than upland- and deciduous species-dominated watersheds, and (ii) forests nearing maturity, export more Hg than young forests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".