Dynamic connectivity assessment for a terrestrial predator in a metropolitan region (data)
Bibliographic record
Abstract
Data used in manuscript, Dynamic connectivity assessment for a terrestrial predator in a metropolitan region, containing coyote steps within the Greater Toronto Area, Canada with vegetation density (NDVI), impervious surface (building density), human population density, and distance to linear features extracted for each start and end step. Linear features labeled following traffic: low (LT), medium (MT), high (HT), and function hiking trails (NT) and public service (PS; (ie. railways and transmission lines). Code Repository: https://github.com/tgelmi-candusso/Dynamic-connectivity-assessment-for-a-terrestrial-predator-in-a-metropolitan-region Coyotes (n = 27; Figure 1) were monitored between 2012 and 2021 for 245 ± 136 days (mean ± standard deviation). Coyotes were live-trapped with padded foothold traps, approved by the Ontario Ministry of Natural Resources Wildlife Animal Care Committee (protocols 75-12, 75-13, 75-14 ) or captured with nets by the Toronto Wildlife Centre, and fitted with self-releasing GPS-collars (Lotek Wildcell SG, Newmarket, Canada), recording location data, resampled following the median sampling frequency in order to maintain a constant sampling frequency for each individual (1-3 hours; Appendix S1: Table S1, http://doi.org/10.1002/fee.2633 ). The data were well balanced in terms of demographic traits (12 females/15 males, 19 adults/eight juveniles, 22 residents/15 transients). Residents and transients were distinguished based on movement characteristics. From consecutive GPS-collar locations, we calculated the turning angle and step length with the steps_by_burst() function from the R package amt (Signer et al. 2019). After fitting the distributions to observed step lengths and turning angles, we generated nine random available steps for each observed step using the random_steps() function from the R package amt (Signer et al. 2019). We standardized the fixed variables included in the model and extracted their values at the endpoint of each step. The fixed variables included four urban landscape covariates: vegetation density (normalized difference vegetation index or NDVI), human population density, impervious surface, and linear features. To measure the spatiotemporal dynamic responses of coyotes, we included the interaction of the fixed variables with three temporal scales (diel cycles, biological seasons, and climate seasons) and three demographic traits (coyote age, sex, and social status). More information on the data and how it was used available at http://doi.org/10.1002/fee.2633
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".