When misconceptions impede best practices: evidence supports biological control of invasive Phragmites
Bibliographic record
Abstract
Abstract Development of a biological control program for invasive Phagmites australis australis in North America required 20 years of careful research, and consideration of management alternatives. A recent paper by Kiviat et al. (Biol Invasions 21:2529–2541, 2019. 10.1007/s10530-019-02014-9 ) articulates opposition to this biocontrol program and questions the ethics and thoroughness of the researchers. Here we address inaccuracies and misleading statements presented in Kiviat et al. (2019), followed by a brief overview of why biological control targeting Phragmites in North America can be implemented safely with little risk to native species. Similar to our colleagues, we are very concerned about the risks invasive Phragmites represent to North American habitats. But to protect those habitats and the species, including P. australis americanus , we come to a different decision regarding biological control. Current management techniques have not been able to reverse the invasiveness of P. australis australis , threats to native rare and endangered species continue, and large-scale herbicide campaigns are not only costly, but also represent threats to non-target species. We see implementation of biocontrol as the best hope for managing one of the most problematic invasive plants in North America. After extensive review, our petition to release two host specific stem miners was approved by The Technical Advisory Group for the Release of Biological Control Agents in the US and Canadian federal authorities.
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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 | 0.326 | 0.741 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".