Determining the growth responses of Phyla canescens to shoot and root damage as a platform to better-informed weed-management decisions
Bibliographic record
Abstract
Understanding the responses of invasive plants to control methods is important in developing effective management strategies. Lippia (Phyla canescens (Kunth) Greene : Verbenaceae) is an invasive, perennial, clonal forb for which few control options exist for use in the Australian natural and agro-ecosystems it threatens. To help inform management decisions, lippia’s growth responses to damage it may experience during proposed control measures, i.e. cutting, crushing, twisting, were assessed in three glasshouse experiments using either whole plants or plant pieces. Plants quickly recovered from severe damage through growth from shoot and root buds at stem nodes. After shoot and root removal, the relative growth rate of the remaining plant was twice that of controls, suggesting tolerance to damage. Lacking buds, root pieces and isolated stem internodes were incapable of responding. Crushing and cutting individual ramets and plant pieces induced the largest responses, including release of axillary buds on damage or removal of apical buds, but full recovery was not achieved. Lippia will be difficult to control because of its ability to rapidly propagate from stem fragments possessing undamaged or damaged nodes; thus, the full impact of control methods that increase fragmentation (e.g. grazing) should be assessed before implementation. Our results also suggest that the most effective biological agents will be those that limit lippia’s vegetative growth and spread, such as shoot- or crown-feeding insects.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".