Genetic variation in tolerance to defoliation in <i>Cirsium arvense</i>
Bibliographic record
Abstract
Summary The perennial weed, Cirsium arvense (creeping, Canada or Californian thistle), is notorious for its ability to tolerate defoliation by mowing, herbivores or herbicides. The tolerance of 36 genotypes of C. arvense was examined by establishing pairs of clonal replicates that were assigned to a clipped or unclipped treatment. Three clippings were applied from spring to early summer to simulate repeated mowing. The average final percentage reduction caused by the repeated clipping was 18%, 72%, 32% and 50% for shoot biomass, root biomass, number of shoots and shoot height respectively. While nearly all genotypes were negatively affected by clipping, some overcompensated, and achieved greater shoot biomass, number of shoots, or increased height than their unclipped counterparts. No genotype was able to overcompensate, or fully tolerate, the lost root biomass due to repeated clipping. Genetic variation for tolerance to defoliation was detected for the number of shoots, maximum shoot height and for relative height growth rate. For relative growth rate, significant genetic variation was not detected until after the third clipping event, indicating that genotypes were equally tolerant to a moderate degree of defoliation, but upon more severe defoliation, genetic differences were evident. Since repeated defoliation is a recommended control technique, selection for more tolerant genotypes is possible and should be considered for the management of this weed.
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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 | 0.001 | 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".