Comparing soil chemistries of leached and nonleached copper-amended soils
Bibliographic record
Abstract
Leaching metal-spiked samples has been proposed as a means to reduce the artifacts of the spiking procedure (e.g., salt effect, increased metal solubility) that can artificially increase metal bioaccessibility and toxicity in laboratory ecotoxicity tests. The effects on soil chemistry from leaching Cu-spiked samples were investigated by comparing chemistries of freshly spiked samples to samples that underwent the spike/leach procedure. Chemical parameters investigated included electrical conductivity (EC), pH, ethylenediaminetetraacetic acid- and CaCl(2) -extractable Cu, soil-solution Cu, Cu(2+) activity (estimated using Visual MINTEQ), and other solution parameters (dissolved organic carbon [DOC], Ca, Mg, Al). In leached samples, the electrical conductivity values of the spiked samples did not vary significantly from those of the control samples (p > 0.05), confirming that the leaching procedure had sufficiently minimized the salt effect. In the range of soil Cu concentrations where Cu ecotoxicity is expected, the pH in freshly spiked samples was as much as 0.52 units lower than the pH from leached samples at the same total-soil Cu concentration. The CaCl(2) -extractable fraction was up to 2.3-fold smaller in leached samples and inversely related to the pH of the spiked soil. Despite little to no difference in soil-solution Cu, up to 100-fold less Cu(2+) activity was observed in leached samples. Reduced Cu(2+) activity was related to less Al(3+) competition for DOC. Leaching resulted in solution chemistries that were more consistent with those of the control samples and reduced the artifacts of traditional soil-spiking procedures.
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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 | 0.001 |
| 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) | 0.001 | 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".