Spatial and temporal occurrences of prairie moose across an urban to rural gradient in Saskatoon, Canada
Bibliographic record
Abstract
The geographic range of North American moose (Alces alces) has expanded over recent decades as the population has recolonized historical habitats and dispersed into new areas, including a notable increase in moose sightings near developed, urban areas. The City of Saskatoon, Saskatchewan is situated on what would traditionally be considered atypical moose habitat, being in the semi-arid, open prairies and surrounded by high-intensity agriculture. However, this area has seen a persistent increase in the frequency of moose occurrences at the urban-rural interface over the last 30 years. We characterized spatial and temporal patterns in moose occurrences over the course of three years (2020-2023) using 29 trail cameras distributed along an urban to rural gradient within the city boundary of Saskatoon. We employed a generalized linear modelling method to assess the potential significance behind where and when moose were occurring along the gradient. Moose occurrence was negatively associated with urban sites containing higher proportions of development (>50% impervious surface cover). While we expected high occurrences during the rut, we found that moose occurrence was low through the fall months. This may be due to the high levels of human disturbance characteristic of the urban-rural interface acting as a deterrent for moose during the breeding season. Future research is warranted to better understand the underlying cause for this result. Moose also occurred most at night, coinciding with the period of lowest visibility and raising concerns for human safety. We provide suggestions and recommendations for future urban moose research and management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".