Reviving common standards in point-count surveys for broad inference across studies
Bibliographic record
Abstract
We revisit the common standards recommended by Ralph et al. (1993, 1995a) for conducting point-count surveys to assess the relative abundance of landbirds breeding in North America. The standards originated from discussions among ornithologists in 1991 and were developed so that point-count survey data could be broadly compared and jointly analyzed by national data centers with the goals of monitoring populations and managing habitat. Twenty years later, we revisit these standards because (1) they have not been universally followed and (2) new methods allow estimation of absolute abundance from point counts, but these methods generally require data beyond the original standards to account for imperfect detection. Lack of standardization and the complications it introduces for analysis become apparent from aggregated data. For example, only 3% of 196,000 point counts conducted during the period 1992–2011 across Alaska and Canada followed the standards recommended for the count period and count radius. Ten-minute, unlimited-count-radius surveys increased the number of birds detected by >300% over 3-minute, 50-m-radius surveys. This effect size, which could be eliminated by standardized sampling, was ≥10 times the published effect sizes of observers, time of day, and date of the surveys. We suggest that the recommendations by Ralph et al. (1995a) continue to form the common standards when conducting point counts. This protocol is inexpensive and easy to follow but still allows the surveys to be adjusted for detection probabilities. Investigators might optionally collect additional information so that they can analyze their data with more flexible forms of removal and time-of-detection models, distance sampling, multiple-observer methods, repeated counts, or combinations of these methods. Maintaining the common standards as a base protocol, even as these study-specific modifications are added, will maximize the value of point-count data, allowing compilation and analysis by regional and national data centers.
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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 | 0.817 | 0.943 |
| Meta-epidemiology (narrow) | 0.005 | 0.011 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.030 | 0.032 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".