Using participatory science to investigate furbearer habitat associations and co-occurrence in Alberta's boreal forest
Bibliographic record
Abstract
Marten ( Martes americana), lynx ( Lynx canadensis), fisher ( Pekania pennanti), and wolverine ( Gulo gulo) are ecologically, culturally, and economically important. We worked with 63 individual trappers across 72 traplines and used remotely triggered cameras at 146 sites to document species occurrence in Alberta, Canada's boreal forest over three trapping seasons. We then evaluated furbearer occurrence as a function of coarse-scale forest harvest, climate, human disturbance, and landcover variables as well as furbearer co-occurrence using a multi-stage generalized linear model framework. Climate and/or anthropogenic-related variables were present in the top models for all species, which may have implications for furbearer distribution as climate and human use patterns change over time. Wolverine occurrence was negatively associated with mean annual temperature, while fisher occurrence was positively associated with it. Landcover was less important; but notably, marten occurrence was negatively associated with conifer forest. Wolverine occurrence was positively associated with lynx occurrence and vice versa. Our work provides an example of successful co-created participatory science. We collected robust data while simultaneously fostering relationships among area trappers and conservation biologists, thereby establishing the foundation for additional collaborative efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".