Transferring decision boundaries onto a geographic space: Agent rules extracted from movement data using classification trees
Bibliographic record
Abstract
Abstract We leverage applied machine learning to determine which environmental features are best associated with the “moving” behaviour(s) of a troop of olive baboons ( Papio anubis ; collared with GPS trackers at Mpala Research Centre, Kenya). Specifically, we develop a behaviour‐selection surface informed by classification trees trained using movement trajectories and remotely sensed environmental features. Atop this surface, we simulate agent movement towards set destinations, constrained by the relative extent to which sets of features are associated with behaviour(s). To achieve our goal, we perform: (a) path segmentation using thresholding to label training data; (b) agent‐rule extraction using classification trees to associate the relative Euclidean distance of a point from environmental features with behaviour; and (c) implementation of this information into an agent‐based model to provide a data‐driven simulation of troop movement. We believe this framework can accommodate intensifications in data velocity, veracity, volume, and variety expected from increasingly sophisticated biologgers and data‐fusion techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".