Assessing functional landscape connectivity for songbirds in an urban environment
Bibliographic record
Abstract
Worldwide, urbanization is recognized as a leading cause of species extinction because of its role in rapid and permanent habitat loss and fragmentation. This study investigates how habitat fragmentation caused by urbanization and transportation corridors affects the movements—and ultimately, the occurrence—of songbirds within a human-impacted landscape. In spring and summer 2005, I used audio playbacks to measure the willingness of birds to cross small-scale features such as roads, railways, rivers, and transportation bridges over riparian corridors within the urban landscape of Calgary, Alberta, Canada. Preliminary results indicate a negative correlation between the likelihood of forest-dependent birds crossing roads, rivers, and bridges over riparian corridors and the width of the gap in vegetation associated with these features. In contrast, railways appeared to be highly permeable for forest birds, probably due to their relatively narrow width. This study is still in its earliest stages. Subsequent phases of the project include: (1) using translocations to measure the permeability of larger-scale elements of the landscape such as freeways and neighbourhoods of various ages and densities, (2) developing individual-based, spatially explicit models aimed at depicting functional landscape connectivity among the city’s natural areas, and (3) exploring the relationship between landscape connectivity and bird species occurrence within these natural areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".