Influence of feral <i>Raphanus sativus</i> seed attributes and burial depth in seedling emergence dynamics
Bibliographic record
Abstract
Abstract Understanding the emergence patterns of agricultural weeds is paramount for effective management practices. This study investigates the emergence dynamics of feral radish ( Raphanus sativus L.), a widespread weed in the Americas, unravelling the intricate interplay of seed attributes—low or no dormancy, darkness requirement, and indehiscent siliques—on seedling emergence. To explore this, we (1) buried isolated seeds and intact siliques at six increasing depths (0–16 cm) under controlled conditions, and (2) evaluated seedling emergence from both isolated seeds and intact siliques placed on the soil surface and buried at 10 cm under field conditions over 3 years. Our findings reveal that seedlings emergence was about five‐fold higher from shallow‐buried than from surficial isolated seeds, and from isolated seeds than from seeds inside siliques, suggesting strong effects of burial depth and the pericarp. Even isolated seeds buried at 12 cm depth outperformed surface isolated seeds. The burial depth influenced emergence patterns; isolated seeds exhibited rapid emergence, with rates five times higher at 10 cm deep compared to those on the soil surface, showing no emergence after year 1. Conversely, the pericarp delayed and staggered seedlings emergence, contributing to the formation of a persistent seed bank for at least 3 years in both burial depths. Seedlings emergence from seeds inside siliques buried at 10 cm begins in year 1 and in those placed on the soil surface year 2, with emergence peaks predominantly in autumn. These findings enrich our understanding of feral radish seedling emergence, providing valuable insights for the development of sustainable and efficient management practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".