Dealing with increasing data volumes and decreasing resources
Bibliographic record
Abstract
The US Naval Oceanographic Office (NAVOCEANO) has recently updated its survey vessels and launches to include the latest generation of high-resolution multibeam and digital side-scan sonar systems, along with state-of-the-art ancillary sensors. This has resulted in NAVOCEANO possessing a tremendous ocean observing and mapping capability. However, these systems produce massive amounts of data that must be validated prior to inclusion in various bathymetry, hydrography, and imagery products. It is estimated that the amount of data to be processed will increase by an overwhelming 2000 times above present data quantities. NAVOCEANO is meeting this challenge on a number of fronts that include a series of hardware and software improvements. The key to meeting the challenge of the massive data volumes was to change the approach that required every data point to be viewed and validated. This was achieved with the replacement of the traditional line-by-line editing approach with an automated cleaning module, and an area-based editor (ABE) integrated with existing commercial off-the-shelf processing and visualization packages. NAVOCEANO has entered into two cooperative research and development agreements (CRADAs) - one with the Science Applications International Corporation (SAIC), Newport, RI, USA, and the other with Interactive Visualization Systems (IVS), Fredericton, N.B., Canada, to integrate the ABE with SAIC's SABER product and IVS's Fledermaus 3D visualization product. This paper presents an overview of the new approach and data results and metrics of the effort required to process data, including editing, quality control, and product generation for multibeam data utilizing targets from digital imagery data and automated techniques.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".