Dynamic connectivity assessment for a terrestrial predator in a metropolitan region
Bibliographic record
Abstract
Protecting wildlife movement corridors is critical for species conservation. Urban planning often aims to create corridors for animal movement through conservation initiatives. However, research on connectivity for urban wildlife is limited. Here, we assessed connectivity for coyotes ( Canis latrans ) dynamically across temporal scales and demographic traits, parametrized using the habitat selection of 27 global positioning system (GPS)‐collared coyotes in the city of Toronto, Canada. The habitat selection models accounted for human population density, impervious area, vegetation density, and distance to different linear features. Results indicated that (1) vegetation‐dense areas were key for connectivity in urban areas; (2) riverbanks, railways, and areas below power lines were predicted as movement corridors; and (3) commercial and industrial clusters strongly disrupted connectivity. Spatiotemporal differences in connectivity were associated with time of day and coyote social status but not with climate and biological seasonality or coyote age and sex. Residential roads were pivotal in the temporal dynamism of connectivity. The maintenance and enhancement of plant structural complexity along key infrastructure (for example, highways, waterways, and parking lots) should be considered when managing connectivity corridors in cities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".