Canada’s Internet-Connected Ocean
Bibliographic record
Abstract
Over fifteen years ago, Ocean Networks Canada (ONC) began with the world’s first large-scale, interactive, real-time portal into the ocean, bringing continuous, real-time data to the surface for applications in scientific research, societal benefits, and supporting Canada’s ocean industry. This marked the dawn of the Internet-connected ocean, enabling a more fulsome understanding of the ocean through ocean intelligence. These open data have improved our ability to monitor and understand our changing ocean offshore all three coasts of Canada, thanks to diversity of sensor systems to monitor earthquakes and tsunamis, deep sea biodiversity, whales, hydrothermal vents, neutrinos, ocean noise, ocean acidification, forensics experiments, and the impact of climate change, including sea ice thinning in the Arctic. This pioneering approach began in the late 1990s, when scientists began developing a new way of doing ocean science that was no longer limited by weather and ship-time. They imagined a permanent presence in the ocean of sensors to allow a continuous flow of ocean data via the Internet. This big science began to take shape early this century, when a partnership between United States and Canadian institutions was established. ONC evolved out of this international collaboration with seed funding from the Canada Foundation for Innovation, while in the United States, the Ocean Observatories Initiative (OOI) was funded. ONC works closely with OOI on that span the countries’ west coast border. Recently similar observing initiatives in Europe have begun, led by EMSO, which now has a close collaboration with ONC as an Associate Member.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.022 |
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".