Wildlife crossing database platform: A transdisciplinary approach to developing a tool for landscape connectivity planning and public engagement
Bibliographic record
Abstract
Abstract Implementing wildlife crossings and restoring landscape connectivity are ecological, social, and economic imperatives. However, in North America, connectivity planning faces challenges associated with governance being fragmented into local jurisdictions and no single agency being solely responsible. Such challenges present a need for tools that can support coordinated actions among different jurisdictions, agencies, and organizations, and effectively promote the importance of landscape connectivity work to diverse audiences. The current study employs a transdisciplinary approach for developing the wildlife crossing database platform (WCDP), a tool for sharing information among practitioners and engaging the public in critical landscape connectivity issues and efforts. The research was conducted in 2 stages: (1) developing a beta version of the WCDP, and (2) engaging diverse practitioners in the field of landscape connectivity to discuss its utility and identify needs for further development. The WCDP was created using Drupal and JavaScript, and it allows users to access, explore, share, and communicate information on wildlife database crossings in North America. A research‐practitioner discussion was convened to discuss the beta version of the WCDP, as well as the key considerations and recommendations for its further development as an effective landscape connectivity planning tool. Data from the researcher‐practitioner discussion were thematically coded and analyzed to reveal 4 major themes with important implications for tool development: (1) differentiate access and functionality between users and the public groups; (2) ensure lessons and success stories are effectively shared; (3) understand the complications and challenges around communicating wildlife crossing information; and (4) recognize landscape connectivity features that do not consist of wildlife crossing infrastructure. Our research produced valuable insights on considerations, challenges, and needs for landscape connectivity work, interprofessional online collaboration, and further refinement of the platform.
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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 | 0.002 | 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".