Bibliographic record
Abstract
In this paper we consider the joint problems of separating and localizing sperm whale click trains.Click train separation is the single-sensor problem of grouping the clicks from each animal together when the clicks of more than one animal are present at a given sensor.Localization is the problem of localizing the animals based on the measurement of time delays of the same click events at multiple sensors.The two problems are inherently connected.We first consider the two problems independently using novel applications of statistical signal processing methods.For separation, we employ an algorithm inspired by the Viterbi algorithm from dynamic programming.For localization, we employ an algorithm inspired by the expectation-maximization (EM) algorithm.Finally, we use the two algorithms to "assist" each other in a joint localization/separation solution.We demonstrate the algorithm on real data. s o m m a i r eEn cet article nous considrons les problmes communs de sparer et de localiser des trains de clic de cachalot.La separation de train de clic est le problme de simple-hydrophone de grouper les clics de chaque animal ensemble quand les clics de plus d'un animal sont prsents une hydrophone donne.La localisation est le problme de localiser les animaux bass sur la mesure du temps retarde des mmes vnements de clic aux sondes multiples.Le problme deux sont en soi relies.Nous considrons d 'abord les deux problmes employant indpendamment des applications de nouveaux des mthodes statistiques de traitement des signaux.Pour la sparation, nous utilisons un algorithme inspire par l 'algorithme de Viterbi de la programmation dynamique.Pour la localisation, nous utilisons un algorithme inspire par l 'algorithme de E-M.En conclusion, nous employons les deux algorithmes pour nous aider dans une solution du joint localization/sparation. Nous dmontrons l 'algorithme sur de vraies donnes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".