A toolbox to quantify human activity in protected areas for park management
Bibliographic record
Abstract
1. Recreation in protected areas (PAs) is growing worldwide, potentially conflicting with wildlife and ecosystem protection. Efficiently estimating human activity in PAs is crucial for balancing a dual mandate of supporting visitor access and biodiversity, but managers lack clear recommendations about how best to monitor spatial and temporal trends in human activity. 2. Through two case studies, we reviewed several key tools for measuring human activity in PAs to assess the impacts on wildlife: camera traps, day passes, trail counters, and social media. We measured human activity across multiple scales and compared spatial and temporal activity estimates within and between PAs. 3. We found strong correlations between tools across PAs and a combination of tools may be better suited to understand finer-scale trends within parks. Individual tools, and their combination, can be tailored to specific research and management goals. 4. Synthesis and applications: Our case studies provide insights into the effectiveness of tools for measuring human activity in PAs and informs practitioners and researchers about how they can be used to address real-world management decisions. Tools varied in their strengths and their weaknesses and looking forward, the widespread adoption of multiple, integrated measures of human activity is needed to develop evidence-based park management strategies, benefitting both humans and nature.
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.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".