Nest site selection and breeding biology of Western House Wrens (<i>Troglodytes aedon parkmanii</i>) using natural cavities in Western Canada
Bibliographic record
Abstract
House Wrens (Troglodytes aedon Vieillot, 1809) are among the best studied songbirds in North America, but most of what is known about this model species derives from studies using artificial nestboxes. Consequently, we know comparatively little about the natural breeding biology of House Wrens and whether it corresponds to patterns reported from nestboxes. To address these issues, we report a study of nest site selection and breeding phenology in Western House Wrens (Troglodytes aedon parkmanii Audubon, 1839) using natural cavities in aspen forests in southwest Alberta, Canada. A total of 96 breeding pairs, representing 77 different banded males, were studied across a 4 year period (2011–2014). In total, 78% of arriving males paired, with 52% of nests successfully fledging. More than 30% of males attempted polygyny, but only 8% succeeded. Distinct patterns were observed for many characteristics of the nest site and cavity, including the type of tree used, as well as the cardinal direction and dimensions of the cavity entrance, its location on the nest tree, and its height above the ground, some of which were related to pairing and breeding success. Results are compared with studies of House Wrens using artificial nestboxes with broader application to many other model species likewise studied primarily using nestboxes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".