Exploring the role of vision in social foraging: What happens to group size, vigilance, spacing, aggression and habitat use in birds and mammals that forage at night?
Bibliographic record
Abstract
I examined the role of vision in social foraging by contrasting group size, vigilance, spacing, aggression and habitat use between day and night in many species of birds and mammals. The literature review revealed that the rate of predation/disturbance was often reduced at night while food was considered more available. Social foraging at night was prevalent in many species suggesting that low light levels at night are not sufficient to prevent the formation and cohesion of animal groups. Group sizes were similar or larger at night than during the day in more than half the bird populations and in the majority of mammal populations. Factors such as calls, feeding noises or smells may contribute to the formation and cohesion of groups at night. Larger numbers of foragers at night may also facilitate the aggregation of more foragers. Vigilance levels were usually lower at night perhaps as a response to the lower predation risk or to the decreased value of scanning for predators that are difficult to locate. Low light levels may also make visual cues that promote aggression less conspicuous, which may be a factor in the lower levels of aggression documented at night. Spacing varied as a function of time of day in response to changes in foraging mode or food availability. Habitats that are avoided during the day were often used at night. Foraging at night presents birds and mammals with a new set of constraints that influence group size, time budgeting and habitat use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".