Whose line is it anyway? Moose (<i>Alces alces</i>) response to linear features
Bibliographic record
Abstract
Abstract Linear features are pervasive across the boreal forest of Canada, negatively impacting several wildlife species. Understanding how wildlife responds to different types and characteristics of linear features is necessary for coordinated landscape restoration. Currently, linear feature restoration is focused on recovering threatened woodland caribou ( Rangifer tarandus ) which may have unintended impacts on other boreal species like moose ( Alces alces ). Understanding how moose respond to different linear features can help ensure restoration is targeted and effective. We used GPS data from seven moose collected between 2008 and 2010 to investigate response to linear features and to determine how moose response was influenced by characteristics of linear features like the surrounding habitat and regeneration. At the landscape scale, moose selected areas closer to seismic lines when they were in areas with lower densities of seismic lines and higher densities of harvest blocks and wildfires. This response was stronger during winter. Moose selected areas closer to pipelines when they were in areas with lower densities of other linear features, harvest blocks, and wildfires and were indifferent to roads at the population‐level. At the fine scale, during winter, moose selected areas closer to seismic lines regardless of vegetation height or the surrounding habitat, but were indifferent to seismic lines during summer, and were indifferent to roads and pipelines during summer and winter. Combined, our results suggest that there are characteristics of seismic lines which make them attractive to moose regardless of the regeneration height on the seismic lines, providing further evidence that effective linear feature restoration will need to address the fact that linear features increase landscape permeability and provide forage for multiple boreal wildlife species. Our results also further illustrate the importance of considering how linear feature restoration efforts focused on caribou may shift the distribution of other boreal wildlife species. Ultimately, conservation efforts for threatened species should recognize that conservation efforts focused on one species may have unintended consequences for interacting species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".