Describing breeding territories of migratory passerines: suggestions for sampling, choice of estimator, and delineation of core areas
Why this work is in the frame
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Bibliographic record
Abstract
1 The goals of this study were to investigate the possibility of using kernel techniques to estimate male breeding territory size and delineate core areas, focusing on a small nontransmitter bearing bird, the cerulean warbler. We then compared the performance of kernel estimators with traditionally used minimum convex polygons (MCP). 2 Given the lack of a consistent across-male sample size–area relationship, we opted to use each male's full set of locations in the kernel calculation rather than standardizing sample size across males. 3 All locations collected for each male were biologically independent though statistically autocorrelated. Subsampling locations did not achieve independence even at time intervals far exceeding biological independence. 4 The physical space bounded by kernel and MCP methods differed drastically in certain cases, especially in situations where there were large areas within a territory that were never visited during our data collection sessions. 5 Kernel methods of territory estimation were far more accurate and informative than MCP for cerulean warblers. We suggest that evenly sampling individuals in a biologically relevant manner during a strictly defined study period is more important than standardizing sample size across individuals. Furthermore, sampling regimes can safely be guided by biological vs. statistical independence timelines. 6 Avian biologists should consider kernel estimators as an option especially for habitat selection studies where accurate territory boundary and size estimation is crucial.
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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 | 0.000 | 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