Time-Frequency Segmentation of Northern Fur Seal Diving Behavior Using Autoregressive Spectral Analysis
Bibliographic record
Abstract
Analyzing marine animal movement data is essential for understanding at-sea behavior. This study introduces an autoregressive spectral analysis framework for assessing time-varying movement dynamics in northern fur seals. A time segmentation approach, based on an AR(3) model with Yule-Walker estimation, is employed to estimate movement parameters and characterize behavioral variability over time. The method captures temporal changes in movement persistence and oscillatory patterns, enabling the segmentation of behavioral states such as resting, exploratory diving, and active foraging. Using high-resolution vertical velocity data from eight northern fur seals tagged at the Pribilof Islands, Alaska, the analysis shows that a 26-minute window with 50% overlap achieves stationarity while preserving behavioral transitions. Spectral analysis identifies mid-frequency oscillations associated with active diving, low-frequency signals corresponding to exploratory diving, and spectral shifts indicative of behavioral transitions. Comparisons with non-parametric methods highlight the advantages of AR(3)-based spectral estimation in producing smooth and interpretable frequency-based insights. The framework provides new perspectives on fur seal foraging strategies and behavioral adaptations to environmental conditions. It also offers a computationally efficient alternative to state-space models, with potential for broader application in studying movement patterns of marine predators to support ecological and conservation research.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".