The acoustic multifrequency classification of two sympatric euphausiid species (Meganyctiphanes norvegica and Thysanoessa raschii), with empirical and SDWBA model validation
Bibliographic record
Abstract
Abstract McQuinn, I. H., Dion, M., and St. Pierre, J.-F. 2013. The acoustic multifrequency classification of two sympatric euphausiid species (Meganyctiphanes norvegica and Thysanoessa raschii), with empirical and SDWBA model validation. – ICES Journal of Marine Science, 70: 636–649. The ecosystem approach to fishery management requires monitoring capabilities at all trophic levels, including pelagic organisms. However, the usefulness of active acoustics for ecosystem monitoring has been limited by ambiguities in the identification of scattering layers. Increasingly, multifrequency acoustic methods are being developed for the classification of scattering layers into species or species groups. We describe a method for distinguishing between sympatric northern and Arctic krill (Meganyctiphanes norvegica and Thysanoessa raschii) using sv amplitude ratios from 38, 120, and 200 kHz data which were pre-processed through a self-noise removal algorithm. Acoustic frequency responses of both euphausiid species were predicted from species-specific parameterizations of a SDWBA physical model using specific body forms (shape, volume, and length) for Arctic and northern krill. Classification and model validation were achieved using macrozooplankton samples collected from multiple-sampler (BIONESS) and ringnet (JackNet) hauls, both equipped with a strobe light to reduce avoidance by euphausiids. SDWBA frequency responses were calculated for a range of orientations (± 45°) and compared with observed frequency responses, solving for orientation by least squares. A tilt angle distribution of N[9°,4°] and N[12°,6°] for T. raschii and M. norvegica, respectively resulted in best fits. The models also provided species-specific TS–length relationships.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".