DISTRIBUTION OF HEAVY METALS IN CONTAMINATED SURFACE WATERS AND ALKALINE TAILINGS WITH TYPHA LATIFOLIA IN A WETLAND ENVIRONMENT, CROSSWISE LAKE, COBALT, ONTARIO 1
Bibliographic record
Abstract
Crosswise Lake hosts the largest accumulation of alkaline tailings in the Cobalt silver mining camp. Tailings were deposited in the north end of the lake by at least five different mills operating between 1908 and 1970. Tailings now blanket the entire lake floor and a lowland through which Farr Creek drains Crosswise Lake and Mill Creek drains surface water bodies from the Cobalt Lake part of the camp. The northern portion of these tailings has been flooded by construction of a water-control dam to form a permanent wetland in which Typha latifolia is the dominant species. Water flowing through this wetland carries elevated concentrations of arsenic and many heavy metals, including cobalt, copper, lead, molybdenum, nickel, and zinc. Sampling of T. latifolia leaves and roots in the wetland indicate that the plants are elevated in most elements compared to background samples, with the roots generally being higher than the leaves. Element concentrations in the roots were less than 15% of the average values for sediment surrounding the roots, while most metals in the leaves generally had concentrations less than 15% of the root values. Molybdenum concentrations were the exception, averaging 85% of the tailings sediment value in the roots and 5x the sediment value in the leaves. The average Mo concentration in the background sediment was twice that of the tailings sediment and leaf values averaged 50% of the sediment average value. For some elements (Ag, Cd, Cr, Cu, Pb, Sb and Zn) the concentrations in the background leaf samples were as high as the tailings leaf samples, even though the background sediment had lower concentrations than the tailings.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".