Detection and Classification of Marine mammals using an LFAS system
Bibliographic record
Abstract
World wide a concern is emerging about the influence of man-made sound in the sea on marine life, and particularly about high power active sonars systems. Most concern lies with marine mammals, which fully depend on sound in their natural behaviour (foraging, navigation and communication). One of the sonars under debate is the Low Frequency Active Sonar (LFAS). This type of system is designed for long range detection of submarines. It consists of a powerful source and a towed array receiver. Incidents with marine mammals could be avoided if the receiver that is dedicated to detection of submarine echoes, is equipped with Detection, Classification and Localisation capabilities for marine mammals as well. In this paper the development of a prototype transient detector and classifier for the TNO-FEL LFAS array (named CAPTAS) is described. A broadband beamformer is developed that creates 8 beams (sectors) that are equally wide over the whole frequency band. A multi-beam LOFAR display is presented. On the normalised data a Page’s test detector is applied that is “optimum” for signals with unknown duration. Detected transients are sent to a classifier that tries to discriminate between biological and man-made or natural transients. Time-frequency analysis is performed and in the resulting timefrequency plot structures are determined by means of cluster analysis after which the sound is classified. Detection results of the prototype are very good, the Classification module is under development and the Localisation module is part of future research. Part of this research is sponsored by the Royal NetherLands Navy (RNLN).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.002 |
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".