2025 Release - North American Breeding Bird Survey Dataset (1966 - 2024)
Bibliographic record
Abstract
This is an outdated version of the North American Breeding Bird Survey (BBS) dataset that has been superseded by a more recent release. Unless you have a specific need for these archived data, please return to the main page and download the latest data release, which includes all BBS data available to date. The 1966–2024 BBS dataset contains avian point-count data for more than 700 North American bird taxa (species, subspecies/races, and unidentified species groups). Data are collected annually during the breeding season—primarily in June—along thousands of randomly established roadside survey routes in the United States and Canada. Each route is approximately 24.5 miles (39.2 km) long with counting locations spaced at roughly half-mile (800-m) intervals, for a total of 50 stops. At every stop, a volunteer highly skilled in avian identification conducts a 3-minute point count, recording all birds seen within a quarter-mile (400-m) radius and all birds heard. Surveys begin 30 minutes before local sunrise and take about 5 hours to complete. Routes are surveyed once per year. The number of sampled routes has grown from just over 500 in 1966 to roughly 3,000 annually in recent decades. No data are provided for 2020 because BBS field activities were cancelled due to the COVID-19 pandemic, and observers were instructed not to conduct surveys. In addition to count data, the dataset includes survey date; start and end times; start and end weather conditions; observer ID; route identification and location information (country, state, and BCR); geographic coordinates of route start points; and an indicator of run-level data quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.084 |
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".