Describing breeding territories of migratory passerines: suggestions for sampling, choice of estimator, and delineation of core areas
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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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".