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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".