Hydropriming Treatment of Rice Seeds With Microbubble Water
Bibliographic record
Abstract
In rice cultivation, seed emergence and seedling establishment tend to be unstable, and rice plants are likely to lodge during the ripening period in direct seeding, leading to an unsteady yield. Although the possibility of direct seeding in dry paddy fields is being re-examined from the viewpoint of reducing labor, unstable seed emergence and seedling establishment remain as challenges to be dealt with. Therefore, in order to improve unstable seed emergence and seedling establishment, we investigated the effects of hydropriming treatment of rice seeds with microbubble (MB)-water which have effect on promoting plant growth, on emergence and early growth of seedlings. In soil with 50% moisture content, the emergence rate, seedling height, longest root length, aboveground dry weight, underground dry weight, chlorophyll content, and a-amylase activity in seeds primed with MB-water were remarkably higher than those in seeds primed with dechlorinated-water and non-primed seeds. However, no significant differences were observed among the seeds primed the same way in soil with 25% moisture content. These results demonstrate that the hydropriming treatment of rice seeds with MB-water promotes their emergence in soil with 50% moisture content. In near future, we need to investigate seedling emergence of other cultivars hydropriming treatment with MB-water.
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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 | 0.000 |
| 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".