Data for: Neotropical mixed-species bird flocks in a community context
Bibliographic record
Abstract
Mixed-species flocks are an important component of bird communities, particularly in the Neotropics, where flocks reach their highest diversity. The extent to which mixed-species flocks represent unique functional or ecological roles within communities, and how these attributes change over environmental gradients, however, is not well understood. We use a trait-based approach to examine functional aspects of flocking assemblages as they relate to those observed in the larger avian community across a 3000 m elevational gradient. Our results reveal similar ecological strategies among flocking species and the communities in which they occur, at the scale of the regional pool and across elevations. Trait variation in flocking and non-flocking assemblages is structured along two major axes defined by size- and resource-related traits. The trait space occupied by flocking species, however, represents only half (51%) that of the larger community. Similarly, the trait space of flocks across elevations is restricted compared to non-flocking species. The shared trait space across flock types represents small-bodied invertivores foraging in lower forest strata, traits associated with increased vulnerability to predation. The concentration of flocking species in functional trait space suggests high niche packing and either more overlap in ecological strategies or more finely divided niches relative to non-flocking species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.048 |
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".