JMesh -- A Scalable Web-Based Platform for Visualization and Mining of Passive Acoustic Data
Bibliographic record
Abstract
Visual presentation of the outputs from marine mammal detectors is key for efficient mining of large acoustic datasets. Operators must be able to easily navigate through large time series of detections, examine spectrograms, listen to detected sounds, and validate and compare detections for different species over time and space. The JMesh web platform has been designed with these constraints in mind. The interface is organized around three interconnected visualization panels: 1) a geographic interface displays maps showing the total number of detections for each species at all monitoring locations within an adjustable time period, 2) a detection time series plot displays temporal variations of detections for several species at a selected monitoring location, and 3) a multimedia panel allows the user to visualize spectrograms, listen to sounds and validate detections. All three panels are interactive and allow the user to navigate intuitively between them. The platform uses load balancing, microservices orchestration, shared non-relational databases, and virtualization technologies to make the infrastructure fully scalable and expandable from a single server to a resource farm composed of hundreds of hosts. JMesh can display detections from archived data or from real-time acquisition systems. Compatibility with mobile devices through the Bootstrap framework simplifies access to the data while in the field.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".