Evaluation of an automated recording device for monitoring forest birds
Bibliographic record
Abstract
Abstract Monitoring of forest songbirds via auditory detections during point surveys can be enhanced by using preprogrammed recording devices. During May–July 2008, we compared boreal forest bird surveys conducted with SM‐1 bird song recorders (Wildlife Acoustics, Inc.) with field surveys by observers and surveys recorded with the E3A Bio‐Acoustic Monitor Kit (River Forks Research Corp.) in Ontario, Canada, to evaluate the utility of the SM‐1 to generate reliable detections of forest birds. The SM‐1 surveys identified, on average, 8.95 species, 0.76 fewer species per 10‐min point count than field surveys ( = 9.71 species) and 1.26 fewer species than the E3A ( = 10.21 species). SM‐1 surveys also identified on average 11.6 individuals per 10‐min count, 2.5 fewer than field surveys ( = 14.1) and 2.3 fewer than E3A surveys ( = 13.9), respectively. The lower number of SM‐1 detections, however, was less than the reduction in detections made by field surveys later as compared to earlier in the breeding season. This suggests that SM‐1 recorders set up early in the season would detect more birds than field surveys stretching late into the season. Moreover, lower detections with the SM‐1 could be easily offset by collecting an additional 10‐min sample on another day. Most species were detected equally well by all 3 methods with a few exceptions. Unattended recording devices are especially advantageous in situations where the number of experienced observers is limited, where access difficult, where multiple samples at the same site are desirable, and where it is desirable to eliminate inter‐observer, time‐of‐day and time‐of‐season effects. © 2011 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".