Measuring variation in the frequency of group fission and fusion from continuous monitoring of group sizes
Bibliographic record
Abstract
The frequency of group fission and fusion is critical for understanding the evolution of sociality, as it measures group cohesiveness, which can confer a high fitness value on individuals who practice it. Variations in the frequency of fission and fusion can also reveal an individual’s response to environmental changes. As fission–fusion group dynamics influence the risk of disease transmission, measuring it accurately may have implications for population management. Here, we extend a method to assess the frequency of fission and fusion from continuous monitoring of group composition. The former method was based on the variation in group size (i.e., party/subgroup size) within which marked individuals were successively recorded. Upon validation of the method here, we propose a correction, using the coefficient of variation in group size instead of the variation in group size. We performed our study on an enclosed herd of reindeer, Rangifer tarandus. All reindeer were tracked using Global Positioning System (GPS) collars and followed by direct observations. GPS collars allowed us to obtain accurate measures of the frequency of fission and fusion that we used to validate our models. The resulting method best described the temporal variation in fission and fusion frequency without the confounding effect of the variation in group size. This method can easily be applied to other populations where some individuals can be recognized and their group size repeatedly recorded. It will also help in quantifying the 2nd dimension of the fission–fusion group dynamics: the variation in group size.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".