Adding value to big acoustic data from ocean observatories: Metadata, online processing, and a computing sandbox
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
Ocean Networks Canada (ONC) operates ocean observatories on all three of Canada's coasts. The instruments produce 300 gigabytes of data per day with over 600 terabytes archived so far. The majority of this data is acoustic, both passive (335 TB) and active (20 TB). This demonstrates the unprecedented capability of cabled observatories to provide unlimited power and data for high bandwidth, continuous data acquisition. Handling this data is a challenge. Metadata, calibration, quality control, and access must be considered. The volume of data is too great for most users to handle. Even if they could store and process it, data transfer to users' computers is a limiting, and perhaps unnecessary step. To address these challenges, ONC has developed a data portal, known as Oceans 2.0, that includes on-demand user-configurable online previewing and processing and a computing “sandbox” where users can upload their own code to process the data. The data portal is now fully accessible by web services. The sandbox is a contained, secure environment with direct access to the data. This paper will present our experience and best practices, including use cases, from acquisition to adding value to the data with these new computing methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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