Geovisualisation for effective management of invasive species: Bridging the knowing–doing gap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it