Fission-fusion dynamics over large distances in raven non-breeders
Bibliographic record
Abstract
The influence of fission-fusion dynamics, i.e., temporal variation in group size and composition, on social complexity has been studied in large-brained mammals that rely on social bonds. Little is known about birds, even though some species like ravens have recently received attention for their socio-cognitive skills and use of social bonds. While raven breeders defend territories year-round, non-breeders roam through large areas and form groups at food sources or night roosts. We here examined the fission-fusion patterns of non-breeding ravens over years, investigating whether birds meet repeatedly either at the same or at different locations. We combined four large datasets: presence-absence observations from two study sites (Austria, Italy) and GPS-tracking of ravens across two study areas (Austria, France). As expected, we found a highly dynamic system in which individuals with long phases of temporary settlement had a high probability of meeting others. Although GPS-tagged ravens spread out over thousands of square kilometres, we found repeated associations between almost half of the possible combinations at different locations. Such a system makes repeated interactions between individuals at different sites possible and likely. High fission-fusion dynamics may thus not hinder but shape the social complexity of ravens and, possibly, other long-term bonded birds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".