Estimating densities of terrestrial wildlife using passive acoustic recordings: A pragmatic approach using paired human observations
Bibliographic record
Abstract
The use of passive using acoustic monitoring is a rapidly growing field in terrestrial ecology, particularly in ornithology. Much recent work has focused on using recording technologies for large-scale occupancy monitoring, while local scale studies have focused on acoustic localization for density estimation. Density estimation offers numerous advantages for answering key conservation questions but may seem impractical to employ at national or continental scales using current acoustic localization techniques. Here, we describe how paired sampling can be used in conjunction with generalized linear (GLM) or generalized linear mixed models (GLMM) to estimate correction factors (δ) to derive density estimates from single acoustic recorders, provided that paired human observers conduct distance estimation. Thus, our approach provides an alternative to more complicated and expensive methods. We discuss the advantages and disadvantages of our approach and highlight the context in which we see various existing or emerging acoustic density estimation methods being used in the terrestrial environment, given the nature and scale of the ecological question(s) being asked.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".