Growth and Cadmium Content in Lettuce and Swiss Chard Plants as Affected by Zinc, Rock Phosphate and Organic Matter Applications
Bibliographic record
Abstract
POT experiments were performed in the North Carolina State University greenhouse using Phosphogypsum/clay tailings from the Potash Corporation of Saskatchewan (PCS) phosphate mine site which contained about 2:1 Zn:Cd ratio. In the first season, lettuce plants (Lactuca sativa var. Parris Island) were grown on the PCS soil with increased Zn content (2:1, 5:1, 25:1, 50:1, 75:1, 100:1, 125:1, 150:1 and 200:1 Zn:Cd ratio) and with four levels of rock phosphate (0.5%, 1%, 2.5% and 5%). The residual effect of these treatments was determined by growing swiss chard plants (Beta vulgaris var. Lucullus) on the same treated soils. Five Zn:Cd ratios (2:1, 25:1, 100:1, 150:1 and 200:1) were used with the application of 1or 3% of composted leaves to determine the effect of organic material along with Zn applications on swiss chard uptake of Cd. The obtained results indicated that Cd concentration in lettuce plants was significantly and gradually decreased from 69.95 ppm to 29.64 ppm in the plants grown on the original PCS soil and the soil treated with 50:1 Zn:Cd ratios, respectively. Whereas the decrease of Cd concentrations in swiss chard plants was from 40.41 ppm to 32.11 ppm in plants grown on the original PCS soil and the soil with 25:1 Zn:Cd ratio, respectively. Compared with the original PCS soil, the application of 5% rock phosphate significantly decreased Cd uptake from 19.00 ppm to 12.59 ppm and from 35.17 ppm to 28.19 ppm in Lettuce and Swiss Chard plants, respectively. The application of 3% composted leaves generally decreased the amounts of Cd uptake by swiss chard plants. The goal of this study was to decrease the plant uptake of Cd by increasing Zn content with and without the application of organic material and also by rock phosphate application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".