What is the effectiveness of methods for eradicating or controlling abundance and biomass of invasive aquatic plants in Canada? A systematic review protocol
Bibliographic record
Abstract
Abstract Invasive freshwater aquatic plants can have adverse ecological effects on the systems to which they are introduced, changing ecosystem function, threatening native plant species and causing billions of dollars in damage to infrastructure. Additionally, once established, invasive aquatic plants are often difficult to eradicate or control. Given the importance of managing invasive aquatic plants, and the high associated economic costs of doing so, it is essential to determine the relative effectiveness of different control methods. Here, we present a protocol for a systematic review that will estimate the effectiveness of various biological, chemical, habitat manipulations and/or manual/mechanical methods for eradicating or controlling invasive plant abundance and biomass. This systematic review will use published and grey literature, without date restriction, that determines the effectiveness of invasive plant control methods. English‐language searches will be performed using five bibliographic databases, Google Scholar, and networking tools to find relevant literature. Eligibility screening will be conducted at two stages: (1) title and abstract and (2) full text. Studies that evaluate the effectiveness of methods for controlling the abundance or biomass or eradicating invasive plants will be included. A list of plant species currently, or potentially, in Canadian freshwater systems and of management concern will be considered. Included studies will undergo critical appraisal of internal study validity. We will extract information on study characteristics, intervention and comparator details, measured outcomes (abundance and biomass, broadly defined) and effect modifiers (e.g., plant growth pattern or timing of treatments). A narrative synthesis will be used to describe the quantity and characteristics of the evidence base, while quantitative synthesis (i.e., meta‐analysis) will be conducted to estimate an overall mean and variance of effect when sufficient numbers of similar studies are available.
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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 | 0.090 | 0.141 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".