A Comparison of Three Count Methods for Monitoring Songbird Abundance during Spring Migration: Capture, Census, and Estimated Totals
Bibliographic record
Abstract
We compared long-term trends (1984-2001) based on three types of spring migration count data, from the three migration monitoring stations at Long Point Bird Observatory (southern Ontario), for 64 species. The three count methods consisted of daily capture totals from banding, sightings from a daily 1-h count on a fixed route (census), and estimated totals (ETs). The latter were estimates of birds detected in each study area each day, based on results from banding, census, and unstandardized other observations. In the majority of species, ET annual indices were significantly positively correlated with both banding and census indices. Banding was not standardized, and variance of annual banding indices was higher than for other count methods, but trends based on banding alone were similar in magnitude to trends from census alone. Relative to trends based on banding or census alone, ET trends were positively biased, possibly as a result of change in estimation methods over time. Nonetheless, because ETs combine data from a variety of count methods, more species can be monitored, with greater precision, than by using one count method alone. Comparison of trends among stations suggested an influence of habitat change at one location. Biases should be minimized with strict standardization of all component count methods, adherence to a clear protocol for ETs, and management of vegetation to prevent systematic habitat change.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".