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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".