The evolution of skilled hindlimb movements in birds: A citizen science approach
Bibliographic record
Abstract
Abstract The ability to manipulate objects with limbs has evolved repeatedly among land tetrapods. Several selective forces have been proposed to explain the emergence of forelimb manipulation, however, work has been largely restricted to mammals, which prevents the testing of evolutionary hypotheses in a comprehensive evolutionary framework. In birds, forelimbs have gained the exclusive function of flight, with grasping transferred predominantly to the beak. In some birds, the feet are also used in manipulative tasks and appear to share some features with manual grasping and prehension in mammals, but this has not been systematically investigated. Here we use large online repositories of photographs and videos to quantify foot manipulative skills across a large sample of bird species (>1000 species). Our results show that a complex interaction between niche, diet and phylogeny drive the evolution of manipulative skill with the feet in birds. Furthermore, we provide strong support for the proposition that an arboreal niche is a key element in the evolution of manipulation in land vertebrates. Our systematic comparison of foot use in birds provides a solid base for understanding morphological and neural adaptations for foot use in birds, and for studying the convergent evolution of manipulative skills in birds and mammals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".