The Behavioral Ecology of Intermittent Locomotion
Bibliographic record
Abstract
Most physiological and ecological approaches to animal locomotion are based on steady state assumptions, yet movements of many animals are interspersed with pauses lasting from milliseconds to minutes. Thus, pauses, along with changes in the duration and speed of moves, form part of a dynamic system of intermittent locomotion by which animals adjust their locomotor behavior to changing circumstances. Intermittent locomotion occurs in a wide array of organisms from protozoans to mammals. It is found in aerial, aquatic and terrestrial locomotion and in many behavioral contexts including search and pursuit of prey, mate search, escape from predators, habitat assessment and general travel. In our survey, animals exhibiting intermittent locomotion paused on average nearly 50% of their locomotion time (range 6–94%). Although intermittent locomotion is usually expected to increase energetic costs as a result of additional expenditure for acceleration and deceleration, a variety of energetic benefits can arise when forward movement continues during pauses. Endurance also can be improved by partial recovery from fatigue during pauses. Perceptual benefits can arise because pauses increase the capacity of the sensory systems to detect relevant stimuli. Several processes, including velocity blur, relative motion detection, foveation, attention and interference between sensory systems are probably involved. In animals that do not pause, alternative mechanisms for stabilizing the perceptual field are often present. Because movement is an important cue for stimulus detection, pauses can also reduce unwanted detection by an organism's predators or prey. Several models have attempted to integrate energetic and perceptual processes, but many challenges remain. Future advances will require improved quantification of the effects of speed on perception.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".