Data from: Direction matching for sparse movement data sets: determining interaction rules in social groups
Bibliographic record
Abstract
It is generally assumed that high-resolution movement data are needed to extract meaningful decision-making patterns of animals on the move. Here we propose a modified version of force matching (referred to here as direction matching), whereby sparse movement data (i.e., collected over minutes instead of seconds) can be used to test hypothesized forces acting on a focal animal based on their ability to explain observed movement. We first test the direction matching approach using simulated data from an agent-based model, and then go on to apply it to a sparse movement data set collected on a troop of baboons in the DeHoop Nature Reserve, South Africa. We use the baboon data set to test the hypothesis that an individual’s motion is influenced by the group as a whole or, alternatively, whether it is influenced by the location of specific individuals within the group. Our data provide support for both hypotheses, with stronger support for the latter. The focal animal showed consistent patterns of movement toward particular individuals when distance from these individuals increased beyond 5.6 m. Although the focal animal was also sensitive to the group movement on those occasions when the group as a whole was highly clustered, these conditions of isolation occurred infrequently. We suggest that specific social interactions may thus drive overall group cohesion. The results of the direction matching approach suggest that relatively sparse data, with low technical and economic costs, can be used to test between hypotheses on the factors driving movement decisions.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".