Distance estimation error data and code. Data and code from:Van Wilgenburg, S. L., D. T. Iles., and S. Haché. (2025). Bias in density estimates from avian point-count surveys: prospects for post-hoc corrections using calibration data. Ornithological Applications 128: duaf000. https://doi.org/10.1093/ornithapp/duaf079
Bibliographic record
Abstract
We collected field data where we had observers estimate distances to singing birds and other field staff confirm the singing locations of the same birds to examine relationships between estimated versus measured distances. Our objectives were to 1) estimate the magnitude of bias in density estimates for distance sampling models in the presence of distance estimation error, and 2) use separate field data to examine whether post-hoc corrections of EDRs can reduce bias in estimated densities. We apply our post-hoc corrections to simulations and empirical point-count data collected in the boreal forest of Saskatchewan, Manitoba and Northwest Territories, Canada to demonstrate the magnitude of change in density estimates we expect in real-world data..
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.070 |
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".