Composite likelihood inference for analysis of individual animal identification data
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
Individual identification data collection is a common practice in animal behaviour, movement ecology, and conservation biology. While likelihood analysis is widely employed for ecological insights, the complexity of individual identification data, characterized by numerous interdependent individuals and identification times, makes direct likelihood calculation challenging. To address this, we introduce a composite likelihood inference framework. We establish the consistency and asymptotic normality of maximum composite likelihood estimators within this framework. Furthermore, we develop a composite likelihood-based information criterion for model selection, capable of handling complex individual identification data. Our approach is demonstrated through extensive simulations and applied to the northern bottlenose whale population in the Gully, Nova Scotia. This study provides a statistically rigorous framework for individual animal identification models, with potential applications extending beyond whale populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it