Modelling Marten (<i>Martes americana</i>) Movement Costs in a Boreal Forest: Effects of Grain Size and Thematic Resolution
Bibliographic record
Abstract
We investigated landscape resistance to movements of American marten (Martes americana) based on snow-tracking data. We generated movement cost maps of the study area with different grain size, thematic resolution, and habitat-specific resistance to movements. We compared simulated tracks obtained from resistance maps to real tracks plotted along transects that we surveyed in winters 2004 to 2008 at the Montmorency Forest, Quebec, Canada. Simulated tracks were located at the intersection between least-cost paths simulated across the study area and transects. We used nearest-neighbour distances between simulated and real tracks to assess the performance of resistance maps and estimate landscape resistance parameters. Simulations with specified costs to movement for open areas, young forest, and mature forest performed better than simpler resistance scenarios, suggesting that resistance to marten movements differed among those landscape attributes that were considered. Simulations with a map grain size of 100 m performed significantly better than 5, 25, and 300 m, possibly because of gap crossing avoidance. Model performance (compared to null model) was maximal when resistance to movement in open habitat was set to 20 times higher than in mature forest, but uncertainty around this estimate was large. This research demonstrates that presence-only (point) data can be used to parameterize movements using spatially explicit modelling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".