Two listeners detect slightly more birds than a single listener when interpreting acoustic recordings
Bibliographic record
Abstract
Abstract Acoustic recorders are increasingly important for monitoring bird populations and have potential to augment existing monitoring programs such as the North American Breeding Bird Survey (BBS). An advantage of acoustic recordings is that they can be reviewed multiple times by multiple experts, potentially yielding improved estimates of species abundance and community richness. Yet, few studies have examined how frequently successive listeners disagree on acoustic interpretations and how strongly estimates of species richness and abundance are altered when multiple experts review each recording. We assigned multiple expert listeners to interpret recordings at 690 BBS stops, and subsequently assigned second listeners to conduct a review of first listeners’ interpretations. We examined the extent to which listeners agreed with each other and quantified the effect of disagreements on resultant estimates of species occurrence, abundance, and stop-level richness. We also compared estimates from acoustic recordings to those obtained during simultaneous field surveys. Estimates were highly correlated for number of species per stop (r = 0.92) and detection probabilities of species (r = 0.97) based on first and second-listener data. Second listeners disagreed with ~9% of first listeners’ interpretations and added an average of ~15% additional species and 16% additional birds not reported by first listeners. Estimates based on acoustic recordings were also highly correlated with those obtained from field surveys, though listeners were unable to count flocks. A single expert reviewer can provide a reasonable approximation of the relative abundance and species composition of birds available for acoustic detection during BBSs. However, acoustic review by multiple listeners may still be important for species that are rare, difficult to identify, or of high conservation concern.
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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 | 0.041 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".