Analyzing Contacts and Behavior from High Frequency Tracking Data Using the wildlifeDI R Package
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inter‐individual interactions are one of the key factors driving patterns of wildlife movement; however, methods for capturing and analyzing inter‐individual interactions from wildlife tracking data remain limited. Extracting contacts from wildlife tracking data is a challenge owing to the complex spatial and temporal patterns and the volume of tracking data sets. Knowledge of the time and location of contacts are crucial to understanding the spatiotemporal patterns of contacts and how they relate to the environment, individual behavior, and social structure. In this article we introduce a new suite of functions in the wildlifeDI R package for automating contact analysis, summaries, and outputs (e.g., visualizations) from studies tracking many individuals simultaneously, building upon the existing methods for studying interactive behavior between dyads already present within the package. The package has applications to study contact and interaction for the study of animal behavior, social networks, and disease transmission. We demonstrate two applications of contact analysis using the wildlifeDI package: female white‐tailed deer ( Odocoileus virginianus ) contacts and contacts between hunters and male white‐tailed deer. The wildlifeDI package represents a new set of advanced, reproducible analyses to identify and study contacts and interactions in wildlife tracking studies. We designed the analyses and outputs to integrate into existing R analysis workflows to facilitate adoption of the package into a wide variety of wildlife tracking studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it