Working Together for Grizzly Bears: A Collaborative Approach to Estimate Population Abundance in Northwest Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grizzly bears are a threatened species in Alberta, Canada, and their conservation and management is guided by a provincial recovery plan. While empirical abundance and densities estimates have been completed for much of the province, empirical data are lacking for the northwest region of Alberta, a 2.8 million hectare area called Bear Management Area 1 (BMA 1). In part, this is due to limited staff capacity and funding to cover a vast geographic area, and a boreal landscape that is difficult to navigate. Using a collaborative approach, a multi-stakeholder working group called the Northwest Grizzly Bear Team (NGBT) was established to represent land use and grizzly bear interests across BMA 1. Collectively, we identified our project objectives using a Theory of Change approach, to articulate our interests and needs, and develop common ground to ultimately leverage human, social, financial and policy resources to implement the project. This included establishing 254 non-invasive genetic hair corral sampling sites across BMA 1, and using spatially explicit capture-recapture models to estimate grizzly bear density. Our results are two-fold: first we describe the process of developing and then operating within a collaborative, multi-stakeholder governance arrangement, and demonstrate how our approach was key to both improving relationships across stakeholders but also delivering on our grizzly bear project objectives; and, secondly we present the first-ever grizzly bear population estimate for BMA 1, including identifying 16 individual bears and estimating density at 0.70 grizzly bears/1,000 km 2 -the lowest recorded density of an established grizzly bear population in Alberta. Our results are not only necessary for taking action on one of Alberta's iconic species at risk, but also demonstrate the value and power of collaboration to achieve a conservation goal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it