Large‐scale bioacoustic monitoring to elucidate the distribution of a non‐native katydid
Bibliographic record
Abstract
Abstract For animals that produce species‐specific audible sounds, environmental recordings combined with automated acoustic monitoring software (passive acoustic monitoring [PAM]) may be an effective monitoring tool because it allows audio data from many, widely distributed autonomous recording units (ARUs) to be processed in a relatively short period of time. Males of many insect species produce loud, species‐specific mating songs, yet acoustic insects have received less attention from PAM relative to vertebrates. We evaluated the use of PAM to monitor, Roeseliana roeselii (Orthoptera, Tettigoniidae), an acoustic insect that has expanded its range to Alberta, Canada, far outside its naturalized North American range. We analysed environmental recordings from ARUs: (1) at two control sites known to be occupied by R. roeselii and (2) across Alberta established by the Alberta Biodiversity Monitoring Institute (ABMI) to search for new populations. PAM successfully detected R. roeselii at the two control sites, but not at any of the 73 ABMI sites that we analysed. Despite the failure to detect new locations of R. roeselii , our analysis of ABMI environmental recordings detected several other species of acoustic insects, including Orchelimum gladiator , Gryllus sp. and Allonemobius spp. Our results add to the growing body of work showing the feasibility of using PAM for acoustic insects. We make suggestions for how to maximize the effectiveness of this monitoring tool for the conservation and management of singing insects in North America.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".