Environmental processes that control the chemical recovery of arsenic impacted northern landscapes
Bibliographic record
Abstract
Arsenic (As) is a contaminant of global concern because of its toxic and carcinogenic properties.Waste streams associated with gold mining activities have resulted in extensive As pollution of landscapes worldwide.The Yellowknife area in the Northwest Territories, Canada was contaminated by more than half a century of As-bearing atmospheric mining emissions from the roasting of arsenopyrite ore during historical gold ore processing .The environmental footprint from legacy mining emissions persists on the landscape and questions remain about the distribution and fate of As in the local environment.The principal objective of this thesis was to improve our understanding of the environmental processes that control the chemical recovery of As impacted landscapes.Individual field studies were designed to address: a) the distribution, source, and solid-phase speciation of As in Yellowknife area soils; b) the seasonal variation of As in surface waters of shallow lakes; c) the influence of contrasting winter hydrology on the geochemical cycling of As in a shallow lake; and d) the seasonal contributions of terrestrial and aquatic fluxes of As in a contaminated watershed.The combination of mineralogical, geospatial, and statistical tools provided a novel method for determination of geochemical background in soils for areas impacted by legacy mining emissions, including recent unambiguous identification of anthropogenically impacted soils.Year-round investigations of surface waters, sediments, and sediment porewaters provided new insights into seasonal and under-ice processes that influence the mobility and geochemical cycling of As.The evaluation of terrestrial and aquatic As fluxes across a contaminated watershed revealed continued transport of legacy arsenic from catchment soils to lakes, and hydrology mediated within-lake processing and export of arsenic.Thus, climate and hydrology were found to play a fundamental role in the chemical recovery of As impacted lakes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".