Context-specific enhanced invasiveness of<i>Raphanus</i>crop–wild hybrids: A test for associations between greater fecundity and population growth
Bibliographic record
Abstract
Campbell, L. G., Teitel, Z., Miriti, M. N. and Snow, A. A. 2014. Context-specific enhanced invasiveness of Raphanus crop–wild hybrids: A test for associations between greater fecundity and population growth. Can. J. Plant Sci. 94: 1315–1324. Evolution by crop–wild hybridization may create plant lineages with greater population growth rates, dispersal, and persistence than weedy progenitors, depending upon plant density. We asked: (1) how does hybridization affect demography and population growth rate (lambda, λ) and (2) how does density affect demography and λ? Over 1 yr, we followed wild radish, Raphanus raphanistrum and crop-wild hybrid, Raphanus sativus×R. raphanistrum, plants grown in 18 experimental sub-populations (originally derived from three wild and three hybrid populations) to assess contributions of germination, survival, and seed production to λ. We explored genotypic differences in demographic characteristics of low- and high-density populations (from populations grown at unmanipulated densities for three generations), using a LTRE. Hybrid populations had greater λs than weedy progenitors when grown in low (but not high) densities. Seed production was more influential on λ than germination or survival, and seed mortality was least influential. Our results indicate weedy Raphanus populations may be best controlled by limiting seed production, rather than killing plants outright or preventing seeds from germinating. Furthermore, relative invasiveness of genotypes is density-dependent, low-density conditions improving potential invasiveness of hybrid populations. We emphasize that assessing invasive potential of a plant will be more successful if one uses a comprehensive demographic approach accounting for a weed's entire life history.
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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 | 0.001 |
| 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".