Calibrating indices of avian density from non‐standardized survey data: making the most of a messy situation
Why this work is in the frame
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Bibliographic record
Abstract
Summary The analysis of large heterogeneous data sets of avian point‐count surveys compiled across studies is hindered by a lack of analytical approaches that can deal with detectability and variation in survey protocols. We reformulated removal models of avian singing rates and distance sampling models of the effective detection radius ( EDR ) to control for the effects of survey protocol and temporal and environmental covariates on detection probabilities. We estimated singing rates and EDR for 75 boreal forest songbird species and found that survey protocol, especially point‐count radius, explained most of the variation in detectability. However, environmental and temporal covariates (date, time, vegetation) affected singing rates and EDR for 73% and 59% of species, respectively. Unadjusted survey counts increased by an average of 201% from a 5‐min, 50‐m radius survey to a 10‐min, 100‐m radius survey ( n = 75 species). This variability was decreased to 8·5% using detection probabilities estimated from a combination of removal and distance sampling models. Our modelling approach reduced computation when fitting complex models to large data sets and can be used with a wide range of statistical techniques for inference and prediction of avian densities.
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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) | 0.000 | 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