Sound localization for estimating population densities of birds: Progress, challenges, and opportunities
Bibliographic record
Abstract
Autonomous recording units (ARUs) are seeing rapid adoption as a research tool to estimate population densities of birds. One challenge for estimating population density is that the distance to the vocalizing bird is unknown. An emphasis has been placed on assessing the detection radius for different species on ARUs, and more recent work has had success relating sound amplitude to distance. We argue, however, that the true variable of interest is not density, per se, but the relationship between density and habitat type or other variables. In an ideal world, ARUs would collect information on the absolute location of each sound source, rather than the number of each species within earshot of the microphone, since the detection radius of an ARU commonly encompasses multiple habitat types. Sound localization is a promising solution to this problem, with some localization methods allowing sound sources to be pinpointed within 1m of their true location. We will present localization results comprising several thousand songs from several species in the Boreal forest, to highlight how localization data can be translated into estimates of population density. We will also highlight several challenges and opportunities for future contributions in the field of sound localization.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".