Increased pollen source area does not always enhance the risk of pollen dispersal and gene flow in Oryza sativa L
Bibliographic record
Abstract
Abstract Pollen dispersal is one of the main ways of gene flow. In the past years, rice pollen dispersal and gene flow have been well studies. However, there is much dispute whether the risk of pollen dispersal and gene flow continuously increases with the source area. A Lagrangian stochastic model was used to simulate the pollen depositions at different distances from different pollen source areas. The field experiments showed a good fit in the pollen depositions. The larger the source area, the more the pollen grains were deposited at each distance, with the pollen dispersal distance increasing accordingly. However, this effect gradually leveled off as the source area increased. In the large-area of pollen source, we found a significantly higher saturation point for the amount of pollen deposition. Once the source area exceeded 1000 × 1000 m 2 , the pollen deposition no longer increased, even if the source area continued to increase, indicating the “critical source area” of rice pollen dispersal. However, a 100 × 100 m 2 critical source area for conventional rice and hybrid rice was sufficient, while the critical source area for the sterile line was about 230 × 230 m 2 .
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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".