Evaluating the Competitive Ability of Semileafless Field Pea Cultivars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The inclusion of competitive crop cultivars in crop rotations is an important integrated weed management (IWM) tool. However, competitiveness is often not considered a priority for breeding or cultivar selection by growers. Field pea ( Pisum sativum L.) is often considered a poor competitor with weeds, but it is not known whether competitiveness varies among semileafless cultivars. The objectives of this study were to determine if semileafless field pea cultivars vary in their ability to compete and/or withstand competition, as well as to identify aboveground trait(s) that may be associated with increased competitive ability. Field experiments were conducted in 2012 and 2013 at three locations in western Canada. Fourteen semileafless field pea cultivars were included in the study representing four different market classes. Cultivars were grown either in the presence or absence of model weeds (wheat and canola), and competitive ability of the cultivars was determined based on their ability to withstand competition (AWC) and their ability to compete (AC). Crop yield, weed biomass and weed fecundity varied among sites but not years. Cultivars exhibited inconsistent differences in competitive ability, although cv. Reward consistently exhibited the lowest AC and AWC. None of the traits measured in this study correlated highly with competitive ability. However, the highest-yielding cultivars generally were those that had the highest AC, whereas cultivars that ranked highest for AWC were associated with lower weed fecundity. Ranking the competitive ability of field pea cultivars could be an important IWM tool for growers and agronomists.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it