Habitat Segregation Among Songbirds in Old-Growth Boreal Mixedwood Forest
Bibliographic record
Abstract
The foraging behaviour of ten species of insectivorous songbirds — Boreal Chickadee (Poecille hudsonicus), Golden-crowned Kinglet (Regulus satrapa), Ruby-crowned Kinglet (R. calendula), Blue-headed Vireo (Vireo solitarius), Tennessee (Vermivora peregrina), Blackburnian (Dendroica fusca), Magnolia (D. magnolia), Yellow-rumped [Myrtle] (D. coronata), Black-throated Green (D. virens), and Bay-breasted (D. castanea) warblers — was observed in the boreal mixedwood forest of Prince Albert National Park in central Saskatchewan. Birds segregated their habitat use by preferentially foraging in different tree species, and through preferential use of different foraging locations (height and position) within trees.White Spruce (Picea glauca) was used more than expected by Yellow-rumped Warblers, and Golden-crowned and Ruby-crowned kinglets. Tennessee and Magnolia warblers used White Birch (Betula papyrifera), more than expected and Boreal Chickadees and Blue-headed Vireos used Balsam Fir (Abies balsamea) more than expected. Boreal Chickadees, Ruby-crowned Kinglets, and Tennessee, Blackburnian and Yellow-rumped warblers all used the bottom part of trees less than expected, while Blueheaded Vireos foraged near the top of trees less than expected. Large inner branches were avoided by Tennessee, Blackburnian and Yellow-rumped warblers, while Bay-breasted Warblers and Blue-headed Vireos avoided small outer twigs. In conifers, Blackburnian Warblers foraged significantly higher in the trees than all other species except Black-throated Green and Baybreasted warblers. Blackburnian Warblers also foraged significantly higher than Blue-headed Vireos and Magnolia Warblers in deciduous trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".