Does relative time of emergence affect stand composition and yield in a grass–legume mixture? Kura clover (<i>Trifolium ambiguum</i>)–meadow bromegrass (<i>Bromus biebersteinii</i>) and Kura clover–orchardgrass (<i>Dactylis glomerata</i>) mixtures
Bibliographic record
Abstract
Abstract Establishing Kura clover (Trifolium ambiguum) in mixtures with grass species is challenging, because slow growth of clover seedlings results in low competitive ability. This study examined establishment success by altering time of seeding of the grass component to reduce competition with Kura clover seedlings. Two trials, one of Kura clover–meadow bromegrass (Bromus biebersteinii) and the other Kura clover–orchardgrass (Dactylis glomerata) mixtures were planted in Edmonton, Alberta. Grasses were seeded at the same time as the clover, or introduced when the clover reached one true leaf or three true leaves, in the autumn of the planting year or the following spring. Species composition varied significantly between treatments. When sown at the same time, Kura clover contributed 31 and 14% of yield in the establishment year when sown with meadow bromegrass and orchard grass, respectively. Delaying grass sowing until Kura clover had one or three leaves gave a higher percentage of Kura clover compared with planting at the same time. Autumn and spring grass sowing resulted in stands of 78 and 80% clover with meadow bromegrass, and 74 and 67% clover with orchardgrass. Altering the competitive advantage of the grass species to produce a more balanced mixture was successfully achieved by delaying seeding of the grass relative to Kura clover. A long interval before introducing the grass (autumn or following spring), was not successful as established Kura clover seedlings have an increased competitive ability.
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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 | 0.001 | 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".