Bibliographic record
Abstract
Introduction: Vigilance enables an animal to obtain information about the environment but often at a cost of reduced foraging rate. some environmental information may not change rapidly, so vigilance might be safely reduced with familiarity with an area. studies have noted this declinein vigilance with familiarity, but the reason for this decline has not been tested. Methods: I proposed and tested two hypotheses to explain this decline in vigilance. The safe experience hypothesis suggests the probability of a predator being nearby but undetected decreases with time spent in an area, enabling an animal to decrease its vigilance due to the reduced risk. The Visual experience hypothesis suggests that as time progresses vigilant animals acquire more information from their surroundings (e.g. refuge locations) allowing for a decrease in vigilance because an animal would not need to detect a predator as early if reaching a refuge required less time. grey squirrels (Sciurus carolinensis) were used to test these hypotheses by feeding them peanut butter in an apparatus that limited their access to visual information by varying degrees. results: An effect of familiarity was evident by a sharp decline in vigilance rates within trials. squirrels adjusted vigilance postures to the different treatments, but the rate of decline in vigilance was unaffected by treatment. discussion: while vigilance is related to visual information, the decline in vigilance with familiarity is not related to the amount of visual information obtained from the environment, giving provisional support to the safe experience hypothesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".