Elucidating the Distribution of a Non-Native Katydid in Alberta Using Bioacoustics
Bibliographic record
Abstract
Accumulating evidence has shown that climate change is causing shifts in species distributions. Several Orthoptera (grasshoppers, crickets, and katydids) species have been shifting their ranges in response to rising annual temperatures. Bioacoustics is a useful tool for monitoring this shift in populations distributions because Orthoptera produce audible vocalizations and can be captured by recording devices. Recently, Roeseliana roeselii, a species of Orthoptera native to Europe, was discovered near Edmonton, Alberta, outside of its naturalized range in eastern North America. This discovery presents a unique opportunity to elucidate the provincial distribution of R. roeselii by using bioacoustics software. In this project, I used automated audio recognition software to sort through province-wide field recordings from the Alberta Biodiversity Monitoring Institute (ABMI) to evaluate the feasibility of using bioacoustics for R. roeselii in Alberta and report any new records or observations. Using field and lab collected recordings of R. roeselii, an algorithm is created to sort through over 10,000 hours of audio. In all these recordings I was unable to detect R. roeselii calls in the ABMI recording data despite finding multiple populations through field sampling. This project lays the groundwork to better understand R. roeselii’s distribution in North America and comments on the possibility for using automated acoustics for other Orthoptera species in North America. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Kevin Judge Department: Biological Sciences
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".