A novel multi-class support vector machine classifier for automated classification of beaked whales and other small odontocetes
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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. s o m m a i r eLe sonar a t rcemment associ un certain nombre d'vnements de mammifre marin immobilis en eau peu profond.Les Baleines a bec (en particulier le Mesoplodon densirostris) ont t les espces prdominantes impliques dans un certain nombre d'vnements d'immobilisation.La surveillance et l'attnuation des effets du bruit synthtique sur les mammifres marins sont des domaines de recherche actifs.Ce qui est importante de la surveillance et la rduction des effets est la capacit automatiquement de dtecter et classifier des animaux, particulirement les baleines a bec.Cet article prsente une nouvelle mthodologie base sur une machine de support vecteur (SVM) pour automatis le classification de niveau d'espces de petits odontocetes.Le nouveau classificateur, appel le "class-specific support vector machine" (CS-SVM), est compos de SVM binaire multiple o chaque SVM se distingue entre une classe d'intrt et une classe commune de rfrence.Un objectif principal dans le dveloppement du CS-SVM tait de raliser une multi-classe robuste SVM dont l'excution est plus simple que des mthodes existantes de la multi-classe SVM.Un CS-SVM a t form pour identifier le vocalisation de clic de quatre espces des odontocetes incluant des Mesoplodon densirostris.Les donnes de srie chronologique de processus d'algorithme sont traites d'une mode entirement automatise dtectant d'abord et classifiant ensuite des vnements de clic.Les rsultats de l'application de ce classificateur automatis fournis par le "Troisime Atelier Internationale de Dtection, Localisation, et Classification du Mammifres Marins avec les Acoutiques Passive" sont prsents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it