An evidence-based protocol for developing lists for tree planting
Bibliographic record
Abstract
Tree-planting is increasingly being promoted for urban greening, carbon sequestration, and to enhance biodiversity. However, poorly planned and executed tree-planting schemes can inadvertently contribute to biological invasions with detrimental effects on local ecosystems, economies, and human well-being. Therefore, sustainable, rigorous, repeatable, and transparent species selection strategies are needed. We developed a strategic decision protocol for identifying tree taxa suitable for planting schemes, using a multi-criterion approach that integrates national lists of regulated invasive plant species, global evidence of invasiveness, and susceptibility to key pests. Using the Polyphagous Shot Hole Borer (PSHB) invasion in the City of Cape Town, South Africa as a case study, we illustrate the protocol’s application and potential for informing planting decisions. 444 tree taxa currently planted in Cape Town were assessed. Of these, 85 are regulated nationally as invasive species (and are prohibited from use), while 49 met all suitability criteria and were identified as candidates for a planting list (i.e., a safe list). This protocol provides evidence-based guidance for tree-planting to mitigate the risk of tree invasions and to reduce the spread and impact of associated pests and pathogens. This protocol is replicable and adaptable for use in other regions and can support environmental planners and managers in making informed decisions to safeguard ecosystems and optimise ecosystem services (e.g., which trees to plant in restoration initiatives).
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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 | 0.243 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.072 | 0.027 |
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".