Bird-friendly Indianapolis: developing a guide to supporting urban avian populations for the benefit of birds and humans alike
Bibliographic record
Abstract
Bird-friendly cities are an emerging concept in the field of urban planning. Stemming from the biophilic urbanism movement, which seeks to seamlessly integrate nature into all dimensions of urban life, bird-friendly cities emphasize the pivotal role that birds play in natural and built environments. From reducing human stress to maintaining thriving ecosystems, birds offer a range of environmental, economic, and health benefits to communities around the world. Existing literature has explored bird-friendly cities across the globe, from Vancouver to Singapore, but cities in the Midwestern United States, including Indiana, have yet to be comprehensively studied. To ensure that Indiana’s capital city is doing enough to protect native birds and maximize the benefits that birds provide, this study explores the extent to which Indianapolis is a bird-friendly city. Through mapping and analysis, stakeholder interviews, and descriptive inventories, existing conditions are assessed, and Indianapolis is assigned a bird-friendliness rating. This creative project culminates in the creation of a public-facing Bird-Friendly Indianapolis Guide which summarizes key findings and identifies recommendations for future initiatives.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.032 |
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".