Bibliographic record
Abstract
Abstract Diverse groups of animals have adapted to exploiting the unique environment of the forest canopy. The arboreal environment is complex and these animals encounter many unique physical challenges as they move up to and through tree branches. Solving these challenges involves dealing with the physical implications of moving on surfaces of various sizes and surface textures. Understanding how these organisms contend with the environment depends on understanding the fundamental factors involved in moving vertically and horizontally while maintaining stability. In most cases, highly specialised anatomy is required to support adhesion, claw penetration and friction contact through gripping, either with digits, a prehensile tail or the body wall. Body size provides hard limits on the ability to exploit the arboreal environment. In the remarkably agile brachiating apes, their specialised locomotion involves subtle dynamics to exploit the energy‐saving benefits of swinging below their supports – an advantage not available in overground locomotion. Key Concepts To climb, an animal must resist gravity, whether horizontal or vertical to the surface. On a vertical trunk, limbed animals must resist a weight‐induced tipping moment. On a horizontal branch, an animal must stabilise itself to avoid falling, unless it hangs from the branch. Small animals have more supports available to them in climbing and are relatively strong compared to their weight. Animals have evolved various specialisations to attach to surfaces, whether through sticking, clinging or gripping. Brachiation is an economical and adaptable locomotor mode unique to the arboreal environment.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".