The Effect of Altered Soil Moisture on Hybridization Rate in a Crop-Wild System (Raphanus spp.)
Bibliographic record
Abstract
Since plant mating choices are flexible and responsive to the environment, rates of spontaneous hybridization may vary across ecological clines. Developing a robust and predictive framework for rates of plant gene flow requires assessing the role of environmental sensitivity on plant reproductive traits, relative abundance, and pollen vectors. Therefore, across a soil moisture gradient, we quantified pollinator movement, life-history trait variation, and unidirectional hybridization rates from crop (Raphanus sativus) to wild (Raphanus raphanistrum) radish populations. Both radish species were grown together in relatively dry (no rain), relatively wet (double rain), or control soil moisture conditions in Ohio, USA. We measured wild and crop radish life-history, phenology and pollinator visitation patterns. To quantify hybridization rates from crop-to-wild species, we used a simply inherited morphological marker to detect F1 hybrid progeny. Although crop-to-wild hybridization did not respond to watering treatments, the abundance of hybrid offspring was higher in fruits produced late in the period of phenological overlap, when both species had roughly equal numbers of open flowers. Therefore, the timing of fruit production and its relationship to flowering overlap may be more important to hybrid zone formation in Raphanus spp. than soil moisture or pollen vector movements.
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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".