Root Growth and Metal Uptake in Four Grasses Grown on Zinc‐Contaminated Soils
Bibliographic record
Abstract
Depth and area of rooting are important to long-term survival of plants on metal-contaminated, steep-slope soils. We evaluated shoot and root growth and metal uptake of four cool-season grasses grown on a high-Zn soil in a greenhouse. A mixture of biosolids, fly ash, and burnt lime was placed either directly over a Zn-contaminated soil or over a clean, fine-grained topsoil and then the Zn-contaminated soil; the control was the clean topsoil. The grasses were 'Reliant' hard fescue (Festuca brevipila R. Tracey), 'Oahe' intermediate wheatgrass [Elytrigia intermedia (Host) Nevski subsp. intermedia], 'Ruebens' Canada bluegrass (Poa compressa L.), and 'K-31' tall fescue (Festuca arundinacea Schreb.). Root growth in the clean soil and biosolids corresponded to the characteristic rooting ability of each species, while rooting into the Zn-contaminated soil was related to the species' tolerance to Zn. While wheatgrass and tall fescue had the strongest root growth in the surface layers (0-5 cm) of clean soil or biosolids, wheatgrass roots were at least two times more dense than those of the other grasses in the second layer (5-27 cm) of Zn-contaminated soil. When grown over Zn-contaminated soil in the second layer, hard fescue (with 422 mg/kg Zn) was the only species not to have phytotoxic levels of Zn in shoots; tall fescue had the highest Zn uptake (1553 mg/kg). Thus, the best long-term survivors in high-Zn soils should be wheatgrass, due to its ability to root deeply into Zn-contaminated soils, and hard fescue, with its ability to effectively exclude toxic Zn uptake.
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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".