Multifaceted approach to determine rice straw phytotoxicity
Bibliographic record
Abstract
Unharvested rice (Oryza sativa L.) straw gets incorporated into soil and interferes with the growth of the next season's crop. Water-soluble phenolics leached from straw into soil may suppress the growth of the next crop. A study was carried out to investigate (i) the effect of soil treated with rice straw (ashes of burned and unburned) leachates on seedling growth and foliar protein content of mustard (Brassica napus var. toria L.), (ii) the modification of rice straw phytotoxicity with abiotic soil, activated charcoal, and nitrogen solution, and (iii) any change in soil inorganic ions and phenolics after treatment with rice straw leachate. Maximum inhibition in root growth of mustard was observed when it was grown in soil treated with leachate prepared by using 100 g of unburned (71.1%, expt. 1; 60.2%, expt. 2) and ashes of burned straw (53.4%, expt. 1; 31.5%, expt. 2). Compared with the untreated control, an increase was observed in the total phenolic content of soil treated with straw leachate, prepared by taking 100, 80, 60, 40, and 20 g unburned straw. When soils were treated with leachate prepared by taking 100, 80, and 60 g straw, a lower level of inhibition was observed in abiotic soil compared with biotic soil. An opposite trend was observed when soil was treated with leachate prepared by taking 40 and 20 g straw. The addition of charcoal eliminated the inhibitory effects of rice straw leachate when leachates were prepared using 40 and 20 g straw. Inhibitory effects of soil treated with leachate prepared from 100 g straw on root growth of mustard were not eliminated after the addition of nitrogen solution. The present study showed that rice straw leachate interferes with seedling growth of mustard and that water-soluble phenolics play an important role in mustard seedling growth inhibition.Key words: allelopathy, rice straw, rice, mustard, phenolics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".