Dealing with increasing data volumes and decreasing resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it