Fractal analysis measures habitat use at different spatial scales: an example with American marten
Bibliographic record
Abstract
Habitat selection is traditionally assessed by how much time the animal spends in each habitat type; however, one can obtain additional information by analysing the structure of the movement paths. We followed and mapped snow tracks of American marten, Martes americana (Turton, 1806). The new method used to test the movement paths for deviations from a correlated random walk model show that these paths fail the test. This has led to an analysis of fractal dimension versus spatial scale, which showed a natural break in fractal dimension at a scale of approximately 3.5 m, suggesting that marten displayed different responses to their microenvironment in two regions of spatial scale. Marten travel was more direct at scales <3.5 m than at scales >3.5 m. Path tortuousity was affected by habitats at smaller scales but not at larger scales, indicating different responses by marten to their environment at these two ranges of scale. Multiple regression identified canopy closure and presence of conifer in the understory as variables that affect movement patterns at the 1- to 3.5-m scale. Fractal analysis of movement patterns provides a unique approach to examining habitat use, as well as a means to identify the spatial scales at which an animal responds to its habitat.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".