Nocturnal Sleep Behavior and Vigilance of Incubating Common Terns (Sterna hirundo) at Two Inland Breeding Colonies
Bibliographic record
Abstract
Although sleep is necessary for maintaining physiological and cognitive function in birds, nocturnal sleep behavior has yet to be documented for terns. Nocturnal sleep behavior and vigilance of incubating Common Terns (Sterna hirundo) were explored at two colonies, Gull Island (Ontario, Canada) for six years, and Little Island (New York, USA) throughout one breeding season, using ∼1-min interval, time-lapse images from infrared trail cameras. Behavioral posture and vigilance (eye[s] open) of visible study birds were recorded from the images to determine if these differed between the two colonies. Terns utilized two sleeping postures, Back Sleep and Front Sleep, nearly identical to those used by gulls. Differences in the proportion of time spent sleeping between the two colonies were surprisingly large. Terns at Gull Island spent 75% less time in Back Sleep (deep-sleep posture, 7% of the night) than those at Little Island, and 64% of night with their eyes open (vs. <20% at Little Island). Differences between the study sites that may have caused this disparity include predation risk, colony size, vegetation cover and the presence of other nesting waterbirds. Apparent, long-term sleep deprivation at Gull Island may have physiological impacts. Further research is needed to establish causes and effects of differences in nocturnal sleep behavior in Common Terns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".