Bags-of-Features for fish school cluster characterization in pelagic ecosystems: application to the discrimination of juvenile and adult anchovy (<i>Engraulis ringens</i>) clusters off Peru
Bibliographic record
Abstract
Whereas fisheries acoustics data processing mainly focused on the detection, characterization, and recognition of individual fish schools, here we addressed the characterization and discrimination of fish school clusters. The proposed scheme relied on the application of the Bags-of-Features (BoF) approach to acoustic echograms. This approach is widely exploited for pattern recognition issues and naturally applies here, considering fish schools as the relevant elementary objects. It relies on the extraction and categorization of fish schools in fisheries acoustic data. Echogram descriptors were computed per unit echogram length as the numbers of schools in different school categories. We applied this approach to the discrimination of juvenile and adult anchovy ( Engraulis ringens ) off Peru. Whereas the discrimination of individual schools is low (below 70%), the proposed BoF scheme achieved between 89% and 92% of correct classification of juvenile and adult echograms for different survey data sets and significantly outperformed classical school-based echogram characteristics (about 10% of improvement of the correct classification rate). We further illustrate the potential of the proposed scheme for the estimation of the spatial distribution of juvenile and adult anchovy populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".