Site‐specific management is crucial to managing <i>Mikania micrantha</i>
Bibliographic record
Abstract
Summary Increasingly, weeds have been taking on global distributions. With the proliferation of invasive weeds has come the challenge of managing these species over broad geographical regions, with diverse habitats and political jurisdictions. Here, we review the management of Mikania micrantha Kunth (Asteraceae; mile‐a‐minute) throughout its invaded range, extending through most of the Pacific islands and southern and south‐east Asia. Context matters when determining the best course of action for managing M. micrantha, as it has invaded a large variety of agricultural and natural systems. In Queensland, Australia and Florida, USA, M. micrantha has been targeted in relatively successful eradication campaigns, highlighting the importance of early detection and rapid response methods, while elsewhere in its invaded range, populations are either still increasing or showing limited signs of decline. An inter‐regional approach to research and management should incorporate successful management strategies employed throughout the invaded range including, but not limited to, chemical and cultural control practices, manual and mechanical control, classical biological control using the rust fungus Puccinia spegazzinii, plant–plant competition and integrated approaches utilising two or more control methods concurrently. Additional knowledge of M. micrantha genetics is required to determine if management approaches could be fine‐tuned for particular populations. Countries bordering the Mekong River formed a network in 2011 to co‐ordinate the management of invasive species such as M. micrantha. Expanding such a collaborative approach to other regions could further reduce populations of M. micrantha and limit its spread.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".