Emerging directions in acoustic ecology – trends within Canada’s national protected areas system
Bibliographic record
Abstract
A survey of ecologists in Parks Canada’s protected area units (PAU) was conducted to understand the breadth of acoustic ecology applications, particularly current emphases, trends over the past 2 decades, and future trajectories. 87 acoustic projects, in 36 PAU, involve detection of species, monitoring of ecosystems, and to a smaller extent documentation of soundscape, anthropogenic noise and cultural sound. On average these PAU have >3 acoustic projects each; the longest project conducted for 18 years. Focus of projects has evolved across years (through birds, bats, marine, soundscape). Described are emerging directions in acoustic ecology evident in Canadian national PAU, including: enhancing research on most taxa (i.e., aquatic species); improving species detection to identify changing spatial-temporal patterns (e.g., climate, noise); documenting anthropogenic noise impact; comparative analysis of biodiversity changes in soundscapes; and increasing technique efficiencies (e.g., automated detection, broad scales). Acoustics could contribute to PAU research priorities (e.g., arthropod inventory, geophysical rate changes, fragmentation restoration, population dynamics), and objectives (e.g., societal wellbeing, cultural landscape). Needed is commitment to document metadata, secure long-term data storage, and contribute to Open Data to ensure future utility of acoustic information. We hope the identification of these emerging directions help formulate momentum and synergies between agencies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".