A novel multi-class support vector machine classifier for automated classification of beaked whales and other small odontocetes
Bibliographic record
Abstract
Navy sonar has recently been implicated in several marine mammal stranding events.Beaked whales (particulary Mesoplodon densirostris) have been the predominant species involved in a number of these strandings.Monitoring and mitigating the effects of anthropogenic noise on marine mammals are active areas of research.Key to both monitoring and mitigation is the ability to automatically detect and classify animals, especially beaked whales.This paper presents a novel support vector machine based methodology for automated, species level classification of small odontocetes.The new classifier, called the classspecific support vector machine (CS-SVM), consists of multiple binary SVM's where each SVM discriminates between a class of interest and a common reference class.A main objective in the development of the CS-SVM was to realize a robust multi-class SVM whose implementation is simpler than existing multi-class SVM methods.A CS-SVM was trained to identify click vocalization from four species of odontocetes including Mesoplodon densirostris.The algorithm processes time series data in a fully automated fashion first detecting and then classifying click events.Results from the application of this automated classifier to the data sets provided by the 3rd International Workshop on Detection and Classification of Marine Mammals Using Passive Acoustics are presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".