Composition of floating weed seeds in lowland rice fields in China and the effects of irrigation frequency and previous crops
Bibliographic record
Abstract
Summary The diversity and composition of floating weed seed communities were surveyed in 27 sites across the main rice‐growing regions in China with the aim of better understanding weed seed dispersal via irrigation water. Seed of 74 species, belonging to 20 families, were identified from floating matter on the water surface in lowland rice fields. Thirty‐five species from three families: Poaceae (15), Asteraceae (11), and Polygonaceae (9), accounted for 47% of all species identified. Species with seed maturing in the summer accounted for 64% of the weed seed and their mean relative abundance was 0.74. Species richness, Shannon–Wiener index and Pielou evenness index were significantly different among the floating weed seed communities. The diversity of weed seed communities in the Yangtze river valley was higher than that in other sites, and some sites were dominated by only a few weed species, such as Beckmannia syzigachne, Alopecurus aequalis, A. japonicus, and Polypogon fugax. At all sites, the dominant weed seeds reflected the dominant weed species in the previous crop. The 27 sample sites of weed seed communities can be clustered into two groups on the basis of previous crop, either lowland rice or sites with previous crops of winter fallow, winter wheat or oilseed rape. Canonical correspondence analysis (CCA) revealed that irrigation frequency, previous crop, and latitude, but not soil type or longitude, significantly affected species composition. The numbers of floating weed seed species were high in lowland rice fields; composition was affected by previous crops and irrigation frequency. Filtering irrigation water and collecting and removing floating weed seeds from the water surface could be integrated into weed management practices to control weeds in lowland rice fields.
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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 | 0.001 | 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) | 0.001 | 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".