Application of GIS to a Study of Mercury in the Environment,
Bibliographic record
Abstract
A Geographic Information System (GIS), in concert with statistical analysis tools, are used to study the statistical and spatial relationships between mercury (Hg) and dissolved organic carbon (DOC) concentrations in a variety of media in Kejimkujik Park, south-central Nova Scotia, where high Hg concentrations have been found in loons and fish. The sampled media includes soil, humus, till, vegetation and water. Humus and the Ah soil horizon exhibit the highest concentrations of Hg, followed by till and water. The GIS analysis of the various media and the integration of Hg anomaly maps using a simple boolean additive model, has established that anomalous Hg concentrations occur in specific areas within the park. The area around Big Dam Lake over the contact zone between leucogranites and Goldenville rocks, and an area around Big Red Lake over biotite/muscovite-bearing granitoid rocks especially high in K, appear to be anomalous. Anomalous Hg and DOC concentrations in water prima-rily occur southeast of Kejimkujik Lake over sulphide-bearing Halifax Formation slates. These rocks may be a prefer-ential source of Hg (biotite and sulphides as a sink for Hg). More importantly, the granitoids and slates may be more conducive to the formation of wetland environments that are characterized by lower pH and increased DOC. These fac-tors are, perhaps, the main drivers in the bioaccumulation of Hg in the park. Résumé
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".