A classification scheme for mixed-species bird flocks
Bibliographic record
Abstract
The literature on mixed-species flocks references a wide variety of bird associations. These studies, however, have used an array of unstructured characteristics to describe flocks, ranging from the temporal occurrence of flocking to the identity and behavioural features of constituent members, with little consensus on which key traits define and characterize a mixedspecies flock. Moreover, although most studies report species-specific roles, there is no clear consensus about what these roles signify nor how to define them. This lack of consistency limits our ability to compare flocks from different habitats, regions, and species pools. To unify this sizable body of literature, we reviewed and synthesized 538 studies on mixed-species flocks.We propose 13 categories to classify mixed-species flocks using behavioural and physical traits at the flock and participant level, as well as the habitat where the flock occurs. Lastly, we discuss the historical terminology for different species roles and propose definitions to clarify and distinguish among nuclear, leader, sentinel, and flock-following species.We envision that these guidelines will provide a universal language for mixed-species flock research, paving the way for future comparisons and new insight between different regions and systems.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".