Adding value to big acoustic data from ocean observatories: Metadata, online processing, and a computing sandbox
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.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".