North American Breeding Bird Survey Dataset 1966 - 2015, version 2015.1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. Version 2015.1 corrects several small but important data issues present in Version 2015.0; the issues are described in the 2015.1 metadata.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.145 |
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 it