Ocean Networks Canada: Long-term ocean observing on a Northeast Pacific cabled ocean observatory
Bibliographic record
Abstract
Long time series observations in the ocean are rare. In the Northeast Pacific, Ocean Networks Canada (ONC) of the University of Victoria operates a number of permanent cabled ocean observatories. The first was installed in 2006, and they have successfully produced many interdisciplinary high-resolution time series over the years, the longest being over 16 years in duration. The cabled observatories operated by ONC include the VENUS coastal observatory and the NEPTUNE off-shore deep-sea observatory. Each observatory has several sites where an observatory node provides continuous power and high bandwidth communications to a wide range of ocean and geophysical sensors. Various long high-resolution time series will be presented and the assessment of climate, decadal, inter-seasonal, annual, and even daily cycles, variations, and signals will be discussed. Such long time series, including environmental baselines, are key for evaluating physicochemical and biological change in the oceans in response to natural variations and climate change. In this way, recent efforts to leverage our time series data in robust monitoring, measurement, reporting, and verification (M2RV) frameworks in the context of different marine carbon dioxide removal (mCDR) approaches, will also be presented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".