Germination rates of weedy radish populations (<i><scp>R</scp>aphanus</i> spp.) altered by crop‐wild hybridisation, not human‐mediated changes to soil moisture
Bibliographic record
Abstract
Summary Cultivated plants are known to readily hybridise with their wild relatives, sometimes forming populations with weedier life‐history strategies than their progenitors. Due to altered precipitation patterns from human‐induced global climate change, crop‐wild hybrid populations may have new and unpredictable environmental tolerances relative to parental populations, which would further challenge farming and land‐management weed control strategies. To recognise the role of seed dormancy variation in weed invasion, we compared seedbank dynamics of two cross‐type populations (wild radish, Raphanus raphanistrum, and crop‐wild hybrid radish, R. raphanistrum × R. sativus) across a soil moisture gradient. In a seed‐burial experiment, we assessed relative rates of seed germination, dormancy and seed mortality over two years across cross types (crop‐wild hybrid or wild) and watering treatments (where water was withheld, equal to annual rainfall, or double annual rainfall). Weekly population censuses in 2012 and 2013 assessed the frequency and timing of seedling emergence within a growing season. Generally, germination rates were two times higher and seed dormancy was 58% lower in hybrid versus wild populations. Surprisingly, experimental soil moisture conditions did not determine seedbank dynamics over time. Yet, seed bank dynamics changed between years, potentially related to different amounts of annual rainfall. Thus, variation in seedbank dynamics may be driven by crop‐wild hybridisation rates and, potentially, annual variation in soil moisture conditions.
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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".