An AI‐based platform to investigate African large carnivore dispersal and demography across broad landscapes: A case study and future directions using African wild dogs
Bibliographic record
Abstract
Abstract Understanding dispersal patterns and demographic processes is crucial for the development of evidence‐based conservation practices. Obtaining such information relies on the ability to identify and track individuals across spatial and temporal scales relevant to the life‐history events under investigation. This knowledge can be achieved by combining photographic and sighting data collected by various sources with a high accuracy automated individual identification platform. Here, we present the African Carnivore Wildbook (ACW), an AI‐based graphical user interface tool capable of identifying individuals of several African carnivore species and specifically developed to accommodate the above outlined needs. We showcase the ACW functionality using the endangered African wild dog as an example. Pictures collected over an area >56,000 km 2 and submitted to ACW allowed inferences on movement patterns and dispersal at regional and international scales; for instance, transboundary dispersal events >200 km were documented. ACW furthermore enabled monitoring some individuals for >4 years; such information is invaluable for reliable survival analyses. We discuss how the ACW can contribute to data collection at appropriate spatial and temporal scales to support population monitoring, scientific research and management of African wild dogs and other apex carnivores and to the conservation of these charismatic species.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".