Bibliographic record
Abstract
This research seeks to develop new knowledge that will aid landscape architects and others to maintain or enhance biodiversity in urban areas. It reports an investigation into site characteristics that support avian diversity in the region of Vancouver, British Columbia, Canada. The research question was: how can urban or urban - fringe sites, in this region, be designed, planned and managed to support avian biodiversity? To answer this question, local multi-species avian habitat models were developed. Three study sites of similar size, vegetation cover, location and context were used to develop various models using the results of soil, vegetation and bird surveys. Data were spatially referenced in a geographic information system. Site avian diversity was measured using the measures of species richness, breeding bird richness, guild richness, birds of concern, Shannon's diversity index, and the Berger-Parker index. Cluster analysis and logistic regression analysis were used to develop the models, which were validated using another site. The study models were converted into descriptive prescriptions for management. One of the key findings is that site habitat heterogeneity is directly correlated with site avian diversity. Different foraging guilds do favor certain habitat types. Some habitat types, such as the Deciduous Forest and the Shorezone, are highly productive, while others such as second growth Conifer Forest and Shrub Thickets are less productive. In addition, certain groupings of habitat types e.g. Old Field/Salt Marsh/Freshwater Marsh capture the most used habitats of the majority of birds, while others are like Old Field/Deciduous Forest are less productive. At the patch level, the habitat characteristics of edge/area ratio, patch area, canopy tree richness, and foliage height diversity were found to support multiple diversity measures throughout the year. The research asked a number of questions that were not being addressed outside the profession of landscape architecture. While the results may be used by others, the research findings constitute new scientific knowledge that is intended to support more effective landscape architectural practice. The findings have implications for the selection and management of conservation sites and for the design, planning and management of human modified landscapes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".