Development and assessment of automated forest road projection methods using performance metrics relevant for wildlife
Bibliographic record
Abstract
Abstract Context Resource road networks have complex and varied impacts on wildlife and other forest values, yet spatial stochastic models forecasting impacts of forest disturbance rarely include automated road network projections. Hardy et al. (Can J For Res 2023. 10.1139/cjfr-2022-0306) partially addressed this need with a LANDIS-II extension, but there remains a need for tools that can be integrated into other modelling frameworks while identifying a pragmatic balance between achieving ecological relevancy and computational cost. Objectives Our goal is an open source resource road network projection tool that can be easily incorporated into modelling frameworks that assess the implications of forest change for wildlife. We compared the performance of several resource road network projection methods using ecologically relevant metrics. Methods We implemented simple iterative least cost path and minimum spanning tree methods with grade penalties in the open source R roads package. We assessed performance by comparing projections to observed resource road development since 1990 in a mountainous region of British Columbia. Results All resource road projection methods that we tested performed relatively well. Grade penalties improved performance, as did our minimum spanning tree method. However, the minimum spanning tree method required more computing time and memory, so users must weigh the benefits of improved performance against computational costs. Conclusions Our resource road network simulation methods can improve projections of anticipated resource development impacts on wildlife across large areas. Our open source implementation in the R roads package will be useful for improving projections of the cumulative effects of natural and anthropogenic disturbances on wildlife in an era of rapid change.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".