The partitioning of Cd, Cu, Pb and Zn between the solid and solution phase of forest floor horizons in podzolic soils near metal smelters /
Bibliographic record
Abstract
The emission of trace metals (TMs) into boreal forest regions of the northern hemisphere is an important environmental issue due to their potential detrimental impacts on these sensitive ecosystems. One of the foremost factors controlling metal cycling is the chemistry of the overlying organic forest floor of the forest soil. In this thesis we examine the chemistry of forest floor horizons of podzolic soils. Our goal is to improve our ability to predict the partitioning of metals between the soil solid and solution phases. We developed a standard protocol to produce solutions that resemble lysimeter solutions from podzolic soils using air-dried samples. We hypothesized that the stabilization point of the electrical conductivity (EC) of the soil solution is indicative of the point in which soluble salts and organic material precipitated during sampling and storage are removed from the soil particle surfaces. Solutions produced by leaching the soils, once the EC of wash solutions had stabilized, were comparable to lysimeter solutions from the area where samples were collected with respect to the concentrations of divalent cations, pH, EC and dissolved organic carbon (DOC). The applicability of this procedure to trace metal partitioning in forest floors was explored. Laboratory extractions produced partition coefficients (log Kd) similar to observed lysimeter solutions ranging from 3.4 to 3.9 for Cd, 3.4 to 3.9 for Cu, 3.4 to 4.1 for Ni, 4.1 to 5.2 for Pb and 3.2 to 3.5 for Zn. According to a semi-mechanistic regression model based on observed lysimeter concentrations, the metal concentrations in solution were appropriate relative to known factors that influence metal partitioning in soils: pH, the concentrations of total metals and DOC. While chemical characteristics of soils have been consistently observed to play important roles in the partitioning and toxicity of metals we wished to place the importance of the chemical characteristics of soil on mobility and toxicity in context. We interpreted field data that had been collected from transects established with distance from two point source emitters in Rouyn PQ, and Sudbury ON. Canada find developed equations that predict dissolved metal concentrations from total metal concentrations, soil pH, soil organic matter (SOM), and DOC contents. We integrated these equations into a simple box model that calculates changes in the concentration of metals in the organic and upper mineral horizons and includes a loop for vegetative return of metals to the forest floor.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".