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 considérons les problèmes communs de séparer et de localiser des trains de clic de cachalot.La separation de train de clic est le problème de simple-hydrophone de grouper les clics de chaque animal ensemble quand les clics de plus d'un animal sont présents à une hydrophone donnée.La localisation est le problème de localiser les animaux basés sur la mesure du temps retarde des mêmes événements de clic aux sondes multiples.Le problème deux sont en soi relies.Nous considérons d 'abord les deux problèmes employant indépendamment des applications de nouveaux des méthodes statistiques de traitement des signaux.Pour la séparation, nous utilisons un algorithme inspireé par l 'algorithme de Viterbi de la programmation dynamique.Pour la localisation, nous utilisons un algorithme inspireé par l 'algorithme de E-M.En conclusion, nous employons les deux algorithmes pour nous aider dans une solution du joint localization/séparation. Nous démontrons l 'algorithme sur de vraies données.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".