The first 50 years of the North American Breeding Bird Survey
Bibliographic record
Abstract
The vision of Chandler (Chan) S. Robbins for a continental-scale omnibus survey of breeding birds led to the development of the North American Breeding Bird Survey (BBS). Chan was uniquely suited to develop the BBS. His position as a government scientist had given him experience with designing and implementing continental-scale surveys, his research background made him an effective advocate of the need for a survey to monitor pesticide effects on birds, and his prominence in the birding community gave him connections to infrastructure—a network of qualified volunteer birders who could conduct roadside surveys with standardized point counts. Having started in the eastern United States and the Atlantic provinces of Canada in 1966, the BBS now provides population change information for ∼546 species in the continental United States and Canada, and recently initiated routes in Mexico promise to greatly expand the areas and species covered by the survey. Although survey protocols have remained unchanged for 50 years, the BBS remains relevant in a changing world. Several papers that follow in this Special Section of The Condor: Ornithological Advances review how the BBS has been applied to conservation assessments, especially in combination with other large-scale survey data. A critical feature of the BBS program is an active research program into field and analytical methods to enhance the quality of the count data and to control for factors that influence detectability. Papers in the Special Section also present advances in BBS analyses that improve the utility of this expanding and sometimes controversial survey. In this Perspective, we introduce the Special Section by reviewing the history of the BBS, describing current analyses, and providing summary trend results for all species, highlighting 3 groups of conservation concern: grassland-breeding birds, aridland-breeding birds, and aerial insectivorous birds.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".