Comparative use of artificial structures and natural vegetation by birds in a built-up urban area in Ghana
Bibliographic record
Abstract
Our understanding of how birds use human supplementary resources, especially artificial structures and patchy vegetation within urban areas, is limited. Our study compared the use of artificial structures versus natural vegetation by birds in built-up areas in the Cape Coast Metropolitan Assembly (CCMA) in the central region of Ghana. Using point count technique, we recorded bird species and the activities performed as well as the substrates they use in residential and commercial areas within the CCMA. We found that the mean bird abundance that used artificial structures did not differ significantly from those that used natural vegetation. The mean species richness that used artificial structures was found to differ significantly from those that used natural vegetation. The study also found a significant difference in activities performed by bird species and the substrate type used for daily life activities. Across species, birds showed preference for trees, shrubs, and natural vegetation structures for perching, feeding, and singing, whereas artificial structures such as billboards, telecommunication masts, ceilings of buildings, pylons, buildings, opening in street lights, and windows of buildings were preferred for nesting. These results demonstrate that although not a replacement for natural resources, artificial structures, when combined with natural vegetation, could contribute significantly to the survival of urban birds. Conservation practitioners could encourage urban mosaic landscapes of built and green spaces to conserve and restore populations of birds.
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 | 0.001 |
| Science and technology studies | 0.000 | 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".