Avian diversity, assemblages and use of vegetation, mainly by shrub-nesters, in an urban ecosystem
Bibliographic record
Abstract
Urbanization is known to have a negative impact on biodiversity. However, it is possible to increase bird species richness in cities through local actions such as increasing vegetation density and diversity. My first objective was to compare bird density and diversity on the island of Montreal among four urban habitat types: low-density and medium-density residential sectors, and residential and natural parks. A second objective was to determine the presence of bird species assemblages within these four urban habitats and a third was to explore associative relationships among six mainly shrub-nesting bird species and the vegetation they use. Point counts were conducted in each of 103 locations. Environmental variables measured consisted of the type (coniferous versus deciduous), density and height of vegetation within each 1 ha sector. Results revealed a decrease in bird abundance from medium-density residential habitats, residential park, low density residential habitats to natural parks and an increase in diversity from medium density residential habitats, low density residential habitats, residential parks to natural parks. Bird assemblages were determined through correspondence analysis. Most bird species were associated with at least one type of urban habitat. Associations between bird species and vegetation were measured through canonical correspondence analysis. The six focal species associated with shrubs demonstrated different levels of association with different habitat variables.
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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 | 0.000 | 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) | 0.002 | 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".