Using bioacoustics to determine bird community patterns in a post-industrial city
Bibliographic record
Abstract
Urban landscapes experiencing population loss often maintain high quantities of vacant land which cause social stress but also create opportunities for conservation of wildlife, including birds. Understanding how features of the urban environment affect bird communities is needed to support planning and policy that creates more effective biodiversity outcomes. Using acoustic recorders, we explored the factors that affect bird communities in Detroit, MI, based on features in surrounding neighborhoods. We compared Shannon diversity, richness, and acoustic detection of birds at 110 recording sites from 2021 to 2023. We used a generalized linear model approach to determine the moderating effect of variables including Normalized Difference Vegetation Index (NDVI) and density of vacant lots around recording sites on bird community space use. We found increased bird diversity at recording sites surrounded by higher densities of vacant land and evidence that some habitat specialists use these areas more than others. Our results indicate habitat preference for areas with more vacant lots, and general preferential habitat selection for certain features of the urban environment. Understanding how urban bird communities use space in a post-industrial, urban landscape will help inform more effective nature-based solutions and urban plans that balance conservation, health, and social justice goals.
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".