Hexachlorobenzene accumulation in rice plants as affected by farm manure and urea applications in dissimilar soils
Bibliographic record
Abstract
Liu, C. Y., Jiang, X., Fan, J. L. and Ziadi, N. 2013. Hexachlorobenzene accumulation in rice plants as affected by farm manure and urea applications in dissimilar soils. Can. J. Soil Sci. 93: 631-638. The key issue of the environmental effects of hexachlorobenzene (HCB) in soil is its bioavailability. A pot experiment was conducted to evaluate the bioavailability of HCB to roots, shoots and grains of rice (Oryza sativa L.), and to determine the effect of farm manure and urea applications on HCB accumulation in rice plants. Two soils, Hydragric Acrisols (Ac) and Gleyi-Stagnic Anthrosols (An), were used. The HCB concentrations in roots were 12 to 17 and 35 to 48 times those in shoots and grains, respectively. The application of 1 and 2% farm manure to both Ac and An decreased the bioconcentration factor of HCB for rice roots, suggesting that farm manure supply decreased HCB bioavailability. The application of 0.03 and 0.06% urea in both tested soils decreased HCB concentrations in rice shoots and roots; these decreases were attributed to the acceleration of HCB degradation by urea supplies. The effect of farm manure and urea supplies on rice grain uptake of HCB was negligible, owing to the small amount of HCB translocation from roots to grains. Because of the higher HCB degradation rate for An, HCB accumulation amounts in rice plants were lower for An than for Ac. In contrast, the bioconcentration factor of HCB was higher for An, suggesting that HCB bioavailability was higher in An than in Ac. The results show that HCB translocation from rice roots to grains was difficult, and that farm manure, urea and soil type all play important roles in HCB accumulation in rice plants.
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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) | 0.000 | 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 | 0.001 |
| 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".