The effectiveness of non-native fish eradication techniques in freshwater ecosystems: a systematic review protocol
Bibliographic record
Abstract
This systematic review will address the need for having a better understanding of the evidence-base for the effectiveness of different management techniques focussed on the eradication of non-native fish species in the freshwater environment. Many resource management agencies around the world attempt to eradicate non-native fish species to achieve management goals with respect to ecological integrity. There is a need to better understand the effectiveness of each management technique to provide resource managers with the information necessary to effectively manage aquatic resources, and to choose the best technique to yield desired outcomes given different ecological and biological conditions. The findings of this systematic review will inform evidence-based management and conservation activities for resource managers around the globe that deal with non-native fish eradication programs. This systematic review will search for, compile, summarize, and synthesize evidence on the effectiveness of fisheries management techniques used for the eradication of non-native fish species in global freshwater systems. The review will use public search engines and specialist websites, and will include both primary and grey literature. All studies that assess the effectiveness of a fish eradication technique, in freshwater, will be included in the review. Potential effect modifiers will be identified to obtain a better understanding of the factors that affect the success of different eradication techniques, given different environmental conditions and biological factors. Study quality will be assessed to allow for critical evaluation, including study design, confounding factors and statistical analysis. Data will be compiled into a narrative synthesis and a meta-analysis will be conducted where data availability and quality allow.
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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 | 0.086 | 0.102 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".