Acoustic estimation of the biodiversity of fish and invertebrates
Bibliographic record
Abstract
The increase in climate and anthropogenic stressors in the marine environment can lead to important losses in habitat for fishes and invertebrates. Consequently, monitoring marine biodiversity has become a critical task for ecologists. Traditional biodiversity measurements are costly and logistically challenging and there is an increasing need to develop new techniques that are more suitable for long-term and large-scale monitoring. The objective of this work is to assess the efficiency of active and passive acoustics to monitor the presence and diversity of fish and invertebrates. This work uses a multi-instrument platform, deployed on Ocean Networks Canada’s VENUS cabled observatory in the Strait of Georgia (British Columbia, Canada), comprised of a high-definition video camera with a pair of LED lights, a dual-frequency imaging sonar and a hydrophone. Fish and invertebrates are counted and identified using the data from the video camera and sonar. Several acoustic indices such as acoustic complexity indices and similarity sound clusters are computed from the hydrophone data. The time series of these indices estimated from the passive acoustic data are then compared to the camera and sonar recordings to assess the ability of passive acoustics alone to determine the presence and diversity of fish and invertebrates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".