Application of High-Cu Compost to Dill and Peppermint
Bibliographic record
Abstract
A controlled environment experiment was conducted to determine the effect of amending soil with various rates of high-Cu compost (0, 20, 40, and 60% compost/soil by volume) on dill (Anethum graveolens L.) and peppermint (Mentha X piperita L.) yields, on fractionation of Cu and Zn in soils, on elemental composition of soil and tissue, and on the essential oils. The compost contained about 2000 mg kg(-)(1) of Cu. Dill yields were greatest in the 20 or 40% treatments, but peppermint yields were greatest in the 20% treatment. Compost additions increased soil pH and electrical conductivity (EC), HNO(3) extractable soil B, Ca, K, Mg, Mn, P, S, Na, and Pb. Additions of high-Cu compost to soil increased tissue P, S, and Na in both crops and Mn, Mo, and Zn in dill but decreased tissue Ca, Cd, and Fe in both crops and Mn, Mo, and Zn in peppermint, increased Cu in all soil fractions including exchangeable, and increased tissue Cu of dill and peppermint as compared to unamended soil. Addition of 60% of high-Cu compost to soil resulted in 760-780 mg kg(-)(1) Cu in the growth medium. Nevertheless, Cu content in both crops reached only 12 mg kg(-)(1) DW in the 60% compost treatment, which is below the toxicity levels for plants and below the upper chronic dietary exposure for animals. The application of high-Cu compost altered chemical composition of dill and peppermint essential oils, but oils were free of Cu, Zn, Cd, Ni, Cr, and Pb. Results from this study suggest that mature composts with concentrations of Cu and Zn of 2008 and 321 mg/kg, respectively, can be used as a soil conditioner without risk for phytotoxicity or risk of increasing the normal range of Cu and Zn in crop tissue. However, the long-term effect of the accumulation of heavy metals in soils following repeated compost applications needs to be carefully considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".