Not seeing the wood for the (invasive) trees: Visitors’ perceptions of invasive wilding conifers in the New Zealand landscape
Bibliographic record
Abstract
Currently we know little about how visitors perceive invasive species, nor how this may vary across visitor cohorts. Previous research suggests that visitors to natural areas have a low awareness of the impact of invasive species. This note reports on a survey of domestic and international visitors (n = 231) in New Zealand, investigating their awareness of invasive wild conifers and attitudes toward their control. Awareness of the wild conifer problem was generally low, especially among international visitors. There were significant differences between domestic and international visitors, and among visitors of different nationalities for how wild conifers were perceived. International visitors, and particularly those from China or other Asian countries were more accepting of wild conifers in the landscape and less supportive of eradication. The findings have implications for management of invasive species, which requires the support of all stakeholders, including tourists, recreationists and their associated sectors.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".