Nutritional qualities and heavy metals accumulation in grains: A study on lowland irrigated rice with different fertilizer inputs and growing seasons
Bibliographic record
Abstract
Integrated and organic nutrient management has become a focal point of current production systems seeking better perspectives on environmental friendliness. The food quality in such a scenario requires special consideration for ensuring safety during consumption. This study was conducted to understand the grain quality of the Bg300 rice variety grown under three fertilizer input systems: conventional (100% N supply with the recommended by the Department of Agriculture (DOA), integrated (50% of N provided with DOA recommended fertilizer + 25% of N supply with organic fertilizer), and 50% of N provided with organic fertilizer in dry tropical irrigated lowland systems in Sri Lanka. The grains were analyzed for proximate composition (moisture content, ash, protein, fat, fiber, and carbohydrate), micronutrients (Fe, Cu, Zn, and Mn), and heavy metals (As, Cd, and Pb). The experiment was arranged as a randomized complete block design and conducted during five seasons, from the 2018-19 wet seasons to the 2020-21 wet seasons. The highest moisture content and carbohydrate content were reported with the organic system. The ash content and protein content significantly (p<0.05) changed with the respective levels of fertilizer in the three input systems. Cadmium and arsenic micronutrients were detected below the permissible level (0.4 and 0.2 ppm), while lead was detected above the permissible level (0.2 ppm). Integrated and organic systems can be used instead of the conventional fertilizer application method without compromising the quality of the rice grain.
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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 | 0.001 |
| 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".