ROAD WATCH IN THE PASS: WEB-BASED CITIZEN INVOLVEMENT IN WILDLIFE DATA COLLECTION
Bibliographic record
Abstract
The successful development of wildlife-transportation mitigation strategies requires access to timely and accurate information on the spatial and temporal movement patterns of wildlife. Unfortunately, conventional long term monitoring programs can be expensive and time consuming. In addition, expert-based approaches often marginalize local participation and knowledge. Alternative approaches to knowledge generation and information sharing, including mechanisms to collaboratively engage citizens, academics and decision makers offer innovative means to overcome the challenges associated with conventional data collection. To address this challenge in relation to wildlife and transportation issues in the Canadian Rocky Mountains, the Miistakis Institute established a community-based monitoring (CBM) framework for wildlife and transportation issues in the Crowsnest Pass. The Crowsnest corridor consists of a two lane highway, a railway line and five principle settlements. There are plans to upgrade the highway to four lanes due to expected increases in traffic volume. Information on spatial and temporal movement patterns of wildlife through the region is essential for the development of effective mitigation strategies to facilitate movement and reduce collisions with vehicles. Road Watch in the Pass is an innovative framework for connecting researchers, citizen volunteers and decision makers through a CBM project to address wildlife transportation issues. It enables citizens to use an interactive Web-based mapping tool (please see www.rockies.ca/roadwatch) to enter wildlife observations along Highway 3.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".