Using digital recordings and sonogram analysis to obtain counts of yellow rails
Bibliographic record
Abstract
ABSTRACT Autonomous recording units (ARUs) are emerging as a useful technology for the study and monitoring of animals that produce vocalizations. During summer and fall of 2013, we performed a series of experiments aimed at developing sampling protocols to count nocturnally active yellow rails ( Coturnicops noveboracensis ) from sound recordings. Field‐based portions of the work took place in the rural municipality of Foam Lake, Saskatchewan, Canada, in an open landscape where yellow rails can be found during the breeding season; lab‐based portions of the work occurred in Saskatoon, Saskatchewan, Canada. Our objectives were to 1) determine the frequency of yellow rail vocalizations to derive an empirically based sampling interval for counting individual birds; 2) assess the accuracy of yellow rail counts made from recordings; 3) determine the approximate sampling radius of the ARU for detecting yellow rails; and 4) determine the approximate audio volume (“loudness”) of yellow rail calls. We developed a sonogram‐based method for counting individual birds on recordings. Using field recordings of individual yellow rails, we generated recordings with known numbers of calling individuals (i.e., 1–12) and tested the accuracy of the sonogram‐based counts. Regardless of experience, observers were able to determine the number of rails calling with a high level of accuracy, especially when the chorus was composed of ≤6 individuals. From broadcast trials employing multiple ARUs, we found the effective detection radius of calling yellow rails to be between 150 m and 175 m. Although detection radius was influenced by broadcast intensity and ambient conditions, we view this range of distance as a reasonable estimate of the effective sampling radius for the ARUs that we used, which is useful for deriving values of density estimates. Finally, we measured loudness of yellow rail calling at approximately 95 dB; this value is useful to research efforts attempting to mimic actual yellow rails (e.g., call‐broadcast surveys, additional ARU experiments). A combination of the sonogram‐counting method and baseline information on detection radius of the ARU provides a tool that will generate high‐quality data on yellow rail occurrence, abundance, and density. Digital recorders represent a means to rapidly improve survey coverage of yellow rails throughout the species' range. © 2016 The Wildlife Society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".