Geovisualisation for effective management of invasive species: Bridging the knowing–doing gap
Bibliographic record
Abstract
Invasive species are a major threat to protected areas, as they disrupt native ecosystems and contribute to biodiversity loss. Invasive species management is faced with a challenge known as the ‘knowing–doing gap’, which refers to the disconnect between scientific research and its application in conservation efforts. Addressing this challenge requires collaboration between stakeholders (including researchers, managers, policymakers and the public), creating a need for tools that can clearly communicate invasive species and strategies to diverse audiences. Realistic, immersive geographical visualisations (geovisualisations), have the potential to serve a role in bridging this gap. This study engages people with management- and place-based relationships in a provincial park in British Columbia, Canada in the use of a novel geovisualisation tool for supporting invasive species management efforts. Using focus group methods, the research collects insights and perspectives on the usefulness of the developed tool. The results indicate that geovisualisations have the potential to engage and educate stakeholders in management options; however, it is important for geovisualisations to maintain realism and account for the diverse backgrounds of users. The paper concludes with suggestions from study participants on how to improve geovisualisation tools in ways that increase their effectiveness and appeal to park and protected area stakeholders.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".