North American Breeding Bird Survey Dataset 1966 - 2015, version 2015.0
Bibliographic record
Abstract
The 1966-2015 North American Breeding Bird Survey dataset contains avian point count data for more than 700 North American bird taxa (primarily species, but also some races and unidentified species groupings). These data are collected annually during the breeding season, primarily June and May, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are about 24.5 miles (39.2 km) long with counting locations placed at regular intervals, for a total of 50 stops. At each stop, a person highly skilled in avian identification conducts a 3-minute point count, recording every bird seen within a quarter-mile (400-m) radius and every bird heard. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. A route is sampled once per year, with the total number of routes sampled per year growing over time; about 600 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. In addition to avian count data, this dataset also contains date route sampled, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, route location information including geographic coordinates of route start point, and an indicator of sample 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.027 | 0.029 |
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".