Grain Phytic Acid Accumulation of Domestic and Exotic Rice Genotypes in Zinc-Deficient Soil
Bibliographic record
Abstract
Micronutrient malnutrition in humans living in rice growing areas is increasing rapidly due to less absorption of mineral nutrients chelated by phytic acid (anti-nutrients) present in rice grains. A field study was conducted to evaluate the grain phytic acid and zinc (Zn) accumulation of 10 field grown rice (Oryza sativa L.) genotypes on a Zn deficient soil. Both the Zn- efficient (Shua-92, IR-9, Shandar, IR-36, and IR-6) and Zn-inefficient (Sarshar,. UPL-48, Khushboo-95 and RG-120) rice genotypes were included in the study. The two Zn treatments (0 and 15 kg ha-1) were arranged in a two factor randomized complete block design with three replications. Nitrogen (N) and phosphorus (P2O5) were applied at the rate of 120 and 80 kg ha-1. The rice genotypes IR-36, UPL-79, Shandar and Shua-92 were the most Zn accumulators whereas; Sarshar, IR-9 and Khushboo-95 the least accumulator in Zn deficiency. Zinc in-efficient genotype Sarshar was the highest Zn accumulator in response to Zn application. Phytic acid content of rice genotypes was significantly influenced (p < 0.05) by the application of Zn fertilizer. Phosphorus concentration in rice grains decreased with Zn application. Zinc in-efficient genotypes accumulated more phytic acid in their food reserves than Zn-efficient genotypes. Phytic acid: zinc ratio decreased significantly more in Zn-inefficient genotypes as compared to Zn efficient genotypes, with application of Zn fertilizer. Zinc efficient genotype Shua-92 accumulated low concentration of phytic acid. The rice genotypes Shua-92, IR-9, Shandar and IR-36 low accumulators of phytic acid performed successfully and contained higher concentrations of Zn than other genotypes.
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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 | 0.001 |
| 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".