Bibliographic record
Abstract
Once globally abundant ranging across Asia, Europe and North America, grizzly bears ( Ursus arctos ) have been classified as threatened, endangered or vulnerable in most parts of their range (Weilgus 2002). In Canada, the grizzly bear is classified as ‘Special Concern’ by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC 2018); in the contiguous United States, they are listed as ‘Endangered’ under the Endangered Species Act (US Fish and Wildlife Service 2018). From the 1940s to 1960s, habitat loss resulting from expanding human settlements and agriculture (Shelton 2001) combined with increasing negative interactions between people and bears led to the killing of grizzly bears and dramatic decreases in population sizes (McCracken 1957). Habitat loss from industrial land use practices and conflict with people continues to impact grizzly bear populations in Canada (Benn and Herrero 2002; Nielsen et al 2006). Human use and development, such as roads, communities, industrial development and recreational use, impact grizzly bear habitat both inside and outside of protected areas in western Canada (Nielsen et al 2006; Sorensen et al 2015). Grizzly bears in western Canada exist in a multi-use landscape with home ranges often overlapping federal and provincial management agency jurisdictions (eg federal and provincial protected areas, other public lands, and private land; Bourbonnais et al 2013). Each of these jurisdictions has different management responses to grizzly bear behaviour and habitat use detailed in their respective management plans. Primary human use in each of these jurisdictions is also variable (eg recreation, private land use, and industrial or commercial use). As a result, how people react to grizzly bears and their expectations regarding bear management change across the landscape. Grizzly bears with home ranges overlapping multiple jurisdictions must navigate a complex variety of human uses and potential management responses. There are many challenges regarding researching grizzly bear habitat use and activity in areas of human use. Their large home ranges can render data collection challenging across varying spatial scales. The diversity of habitats they can occupy across various densities and intensities of human use can make inferences at the population level difficult to defend. They are also complex animals that can make decisions based on complex stimuli and learn over time, which can render robust statistical analyses at the population scale difficult or inappropriate based on the dataset.
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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 | 0.038 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".