GOALS AND STRATEGIES FOR ESTIMATING TRENDS IN LANDBIRD ABUNDANCE
Why this work is in the frame
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Bibliographic record
Abstract
Reliable estimates of trends in population size are critical to effective management of landbirds. We propose a standard for considering that landbird populations are adequately monitored: 80% power to detect a 50% decline occurring within 20 years, using a 2-tailed test and a significance level of 0.10, and incorporating effects of potential bias. Our standard also requires that at least two-thirds of the target region be covered by the monitoring program. We recommend that the standard be achieved for species' entire ranges or for any area one-third the size of the temperate portions of Canada and the United States, whichever is smaller. We applied our approach to North American Breeding Bird Survey (BBS) data. At present, potential annual bias for the BBS is estimated at ±0.008. Further, the BBS achieves the monitoring standard for only about 42% of landbirds for which the BBS is considered the most effective monitoring approach. Achieving the proposed monitoring target for ≥80% of these species would require increasing the number of BBS—or similar survey—routes by several-fold, a goal that probably is impractical. We suggest several methods for reducing potential bias and argue that if our methods are implemented, potential bias would fall to ±0.003. The required number of BBS or similar routes would then be 5,106, about 40% more than in the current BBS program. Most of the needed increases are in 15 states or provinces. Developing a comprehensive land-bird monitoring program will require increased support for coordination of the BBS (currently 2 people) and new programs for species that are poorly covered at present. Our results provide a quantitative goal for long-term land-bird monitoring and identify the sample sizes needed, within each state and province, to achieve the monitoring goal for most of the roughly 300 landbird species that are well suited to monitoring with the BBS and similar surveys.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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