Birdwatchers Across North America Tend to Survey Birds in the Morning for No Apparent Reason
Bibliographic record
Abstract
Bird surveys are typically conducted in the early morning hours. This time of day is chosen since it presumably reflects the greatest activity of birds: many species are most active around sunrise, making them easily detected. While most bird research calls for strict and standardized protocols for when to survey for birds, such approaches are often labor-intensive and limited to very small spatial scales. A community-based approach (often termed “citizen science”) offers a data-intensive alternative to conventional data collection. This community approach involves gathering data from volunteers who submit observations of birds that they encounter at any point, along with information that describes their sampling effort. Such volunteers are not given any specific instructions as to how and when to collect bird data. In this study, I used observations submitted to eBird—a popular web-based platform where more than 800 thousand birdwatchers from Canada and the U.S. have contributed bird sightings between 2010 and 2023. I tested whether observers were biased on when they were birdwatching. I estimated a time-of-day bias as a deviation of estimated kernel density of solar time for >4 million observations from >30,000 locations across Canada and the U.S. relative to a simulated uniform timing distribution. I found a substantial time-of-day bias across observations wherein a large proportion were submitted immediately after local sunrise. Night observations, however, were scarce and represented only a negligible part of the dataset. In fact,
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".