Trace Metal Distribution and Mobility in Soils after Silvicultural Thinning and Burning
Bibliographic record
Abstract
Silvicultural thinning and burning are common management practices that are widely used to address ecosystem problems such as tree stocking and general forest health. However, high-severity fire has variable effects on soils, resulting in damages which are directly or indirectly reflected on the trace metal chemistry of the soil. This study was conducted to evaluate the trace metal variation at the Bankhead National Forest in Northern Alabama following the silvicultural thinning and burning. The experimental site had treatments consisting of two burning patterns and three levels of thinning as part of an overall treatment of three burning patterns and three levels of thinning applied to nine treatment plots to fit a completely randomized block design experiment. Four treatments sites were used for this study and samples were collected from soil profile pits excavated at representative plots within each treatment. The samples were analyzed for trace metals-As, Cu, Ni, Zn and Pb-using Perkin Elmer 2100 ICP-OES. Post treatment samples indicated that the trace metal concentrations generally decreased with soil depth. Copper, Ni, and Zn at the Pre-burn site gradually increased with depth to a maximum concentration at about 50 cm below the soil surface. Arsenic in the surface horizons increased by 156% in the burn-only sites, 54% in the thin-only treatment, 30% for the burn and thin treatments. Such differences were unlikely due to differences in the geochemistry of the parent material, but likely due to anthropogenic activities and possibly the forest management practices in question.
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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 | 0.000 | 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".