Fly ash and zeolite decrease metal uptake but do not improve rice growth in paddy soils contaminated with Cu and Zn
Bibliographic record
Abstract
Fly ash (FA) and zeolite (Z) are known to reduce bioavailability of metals in soils via immobilization; however, these amendments may not only immobilize metals such as copper (Cu) and zinc (Zn), but also reduce nitrogen (N) and phosphorus (P) availability in the soils via sorption (for N by Z) and precipitation (for P by FA). This study was conducted to evaluate the effects of application of FA and Z (0, 5, and 10% rate) on the availability of nutrients as well as metals in the metal-contaminated soils cultivated with paddy rice ( Oryza sativa L.). Both FA and Z reduced Cu and Zn uptake by rice while increasing metal retention in the soils regardless of the application rates. However, reduced uptake of metals did not translate into increase in rice growth, especially at the 10% of amendment rates, due to decreased nutrient uptake as indicated by higher NH 4 + and available P concentration in the soils amended with Z and FA, respectively, which inhibited tillering in the early rice growth period and thus reduced biomass accumulation at maturity. Our results suggest that FA and Z may reduce Cu and Zn uptake by rice in the soils contaminated with the metals; however, the availability of N and P is likely to be co-decreased. We suggest that the capacities of FA and Z to immobilize nutrients as well as metals need to be considered prior to using the amendments in metal-contaminated rice paddies.
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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) | 0.000 | 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".