Ocean Networks Canada Observatory: The Science and Technology Behind the NEPTUNE and VENUS Cabled Ocean Networks
Bibliographic record
Abstract
NEPTUNE Canada and VENUS are cabled ocean observing systems installed on the west coast of British Columbia. They currently represent the largest such networks in the world, hosting hundreds of sensors under or on the seabed, or in the water column. The presentation will review the purpose of the networks, the technologies they use and the challenges that they represent on a daily basis. Tools to manage the system and use the data will also be presented. The talk should be of interest to anyone working in ocean sciences as well as to engineers in charge of providing solutions that enable the discovery of Earth's inner space. Presenter Bio Benoît Pirenne is NEPTUNE Canada Associate Director, Information Technology at the University of Victoria since October 2004. He is in charge of all Data Management and Archiving aspects (from system development to operations) of both the VENUS and NEPTUNE Canada observatories. He directs a group of 20 computer professionals organized in three teams. Previously, Benoît spent 18 years at the European Southern Observatory (ESO, Munich, Germany), a leading Organization for astronomical research. At ESO Benoît assumed a number of scientific and technical positions. As Head of the Operations Technical Support Department in this Organization, he was responsible for running the Data Management and Archiving system supporting both ESO's telescopes and NASA/ESA's Hubble Space Telescope. Benoît has a BA in computer science from Liège, Belgium, and a Master in computer science from the University of Namur, Belgium.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".