Interspecific Relationships between White Clover, Kentucky Bluegrass, and Canada Thistle during Establishment
Bibliographic record
Abstract
Forage establishment on fallow fields may be reduced by re‐colonization and associated competition from hard to control perennial weeds that reproduce from residual root fragments. We assessed the competitive relationships between regenerating Canada thistle (Cirsium arvense L.) (CT) root fragments and seedlings of white clover (Trifolium repens L.) (WC), and Kentucky bluegrass (Poa pratensis L.) (KBG). Two greenhouse trials were conducted, each comparing 10 combinations of all three species grown in monoculture, or together (1:1:1 ratio), or in 1:2 and 2:1 binary mixtures in all possible combinations. All treatments were additionally done at two planting densities. After establishment, plants were grown for 70 d and assessed for biomass of all three species, as well as thistle shoot densities. Results from the two trials were similar. Canada thistle consistently produced the greatest biomass, and was more susceptible to intraspecific competition than competition from adjacent forage plants. Seedlings of neighboring WC were more effective competitors than KBG in reducing final thistle biomass and shoot density. White clover was also more likely to maintain shoot and root biomass under increasing weed presence. These results highlight the importance of reducing CT populations before forage seeding, as well as incorporating species such as WC that may be more tolerant of competition from the weed during the initial establishment period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".