Artificial intelligence (BirdNET) supplements manual methods to maximize bird species richness from acoustic data sets generated from regional monitoring
Bibliographic record
Abstract
Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods ( Listening, Visual Scanning, Recognizer, and Recognizer with Validation) and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ∼1% of the recordings using Listening and detected 56% of the bird community with 71.5 h of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ∼22% of the effort required for Visual Scanning (56 and 257 h, respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23%–63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring data sets.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".