Building the World's First Regional Cabled Ocean Observatory (NEPTUNE): Realities, Challenges and Opportunities
Bibliographic record
Abstract
NEPTUNE (North-East Pacific Undersea Networked Experiments) will be the world's first regional cabled ocean observatory, covering most of the 200,000 km2Juan de Fuca tectonic plate. After several years of planning, NEPTUNE Canada should complete the installation of the northern part with five observatory nodes in late 2008; the US Congress may approve 6-year installation funding for NSF's Ocean Observatories Initiative this year, resulting in the NEPTUNE US portion becoming operational in about 2013 with probably four observatory nodes. The abundant power, high bandwidth communications, and hundreds of sensors delivering data and imagery in real or near real time will revolutionize our knowledge of the ocean environment and interacting processes. With the world's oceans in a state of crisis, the development of cabled observatory technology is most timely and will offer a data archive of unparalleled importance for new discoveries. NEPTUNE Canada has secured over $85M, mainly from the Canada Foundation for Innovation and the BC Knowledge development Fund, and $17M in-kind support. Several government departments, NSERC, and CANARIE have provided other grants and contributions. The University of Victoria (UVic) leads a consortium of 12 Canadian universities, and is required both to own and operate the observatory. UVic also leads the coastal observatory, VENUS (www.uvic.ca/venus). It has established Ocean Networks Canada as a wholly owned, not-for-profit agency to manage the NEPTUNE Canada and VENUS cabled observatory projects as national facilities. Alcatel-Lucent was contracted to design, manufacture and install the NEPTUNE Canada subsea infrastructure, with a 25-year lifespan.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".