Effects of ground granulated blast-furnace slag (GGBS) on hydrological responses of Cd-contaminated soil planted with a herbal medicinal plant (<i>Pinellia ternata</i>)
Bibliographic record
Abstract
Pinellia ternata is a medicinal herb often contaminated by cadmium (Cd), which inhibits its growth and metabolism. In this study, ground granulated blast-furnace slag (GGBS) is proposed as a soil amendment to reduce plant Cd availability and therefore increase transpiration-induced suction. Field soil (silty gravelly sand) contaminated with 1.5 mg·kg−1 of Cd was collected from Guizhou, China. The growth of P. ternata in 0%, 3%, and 5% GGBS amended soil has been investigated in a temperature- and humidity-controlled chamber. Soil amended with 3% and 5% GGBS significantly (P < 0.05, where P is probability) increased leaf area index (LAI) by 29% and 30%, root area index (RAI) by 65% and 66%, respectively, compared to untreated soil (control). Soil suction was significantly (P < 0.05) increased by 58% in soils amended with 3% and 5% GGBS. Compared to the control, soil amended with 3% and 5% GGBS has exhibited higher air-entry value (AEV) and higher water retention ability. The study revealed that addition of GGBS increased soil water availability for plant growth. This implies that to achieve statistically significant improvement in P. ternata growth and soil hydrological responses 3% GGBS should be applied in Cd-contaminated soil.
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 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) | 0.001 | 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".