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 which often have detrimental effects on local ecosystems, economies, and human well-being. Therefore, sustainable, rigorous, repeatable and transparent species selection strategies need to be employed to maximise ecosystem benefits. Using the Polyphagous Shot Hole Borer (PSHB, Euwallacea fornicatus ) invasion in South Africa as a case study, we used a multi-criterion approach, incorporating national lists of regulated invasive plant species, records of their invasiveness in other regions, and data on PSHB susceptibility to develop a strategic decision protocol for identifying tree taxa that can be safely considered in planting schemes. Using a recently published national planted tree inventory, we apply this protocol to the City of Cape Town, South Africa, a metropolitan municipality experiencing widespread tree and PSHB invasions. Among the 445 planted tree taxa evaluated in Cape Town, 85 are regulated nationally as invasive species (and so must not be used), while 49 are deemed suitable candidates for planting initiatives (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. It can provide support for environmental planners and managers in making informed decisions to safeguard ecosystems and optimise ecosystem services. This protocol is replicable and adaptable for use in other regions and offers a robust model for sustainable planting, restoration planning, and invasive species management.
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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 | 0.229 | 0.291 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.100 | 0.038 |
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".