The use of active and passive acoustics for early detection and control of invasions
Bibliographic record
Abstract
As the threat of global invasions increase it is critically important to develop tools for early detection and control of invasive species. The earlier invasives can be detected the more feasible their control and potential eradication will be. Surveys involve the use of sensors to find and verify the identity of new invaders quickly and efficiently. Here, I summarize what features of acoustic sensors make effective tools for early detection of invasives and present examples of their use in both terrestrial and aquatic environments. Acoustic sensors combined with visual sensors allow for species identification and localization when sounds are unknown or undescribed. Technology for use of passive acoustic sensors for early detection of invasives is still in its infancy, but use will expand as methodology for automatic detection and identification develops particularly in remote settings. Once an invasive species is detected, active acoustic methods can be used to prevent range expansion or facilitate biocontrol. I will present various examples of such control methods from studies targeting fish, bird, mammals and amphibians.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".