Canadian Integrated Ocean Observing System Investigative Evaluations: Observations and Data
Bibliographic record
Abstract
Many countries have employed a coordinated network of government agencies, research institutions, and private companies to establish national integrated Ocean Observing Systems (OOSes). Although Canada boasts a robust and diverse ocean economy, the country has yet to implement such a system. To better adapt in the face of a changing environment and to assist the country in meeting national and international commitments, Fisheries and Oceans Canada (DFO) commissioned investigative evaluations (IEs) to determine the cost and feasibility of creating a Canadian Integrated Ocean Observing System (CIOOS). This report contains the recommendations of the Observations and Data IE, which identified Core Ocean Variables (COVs) that should be monitored to meet the needs of Canadians, and to contribute to Canada’s international commitments to ocean monitoring. Existing regional ocean monitoring capacities across Canada that could contribute to the system were evaluated, and an assessment was conducted of the needs for the stewardship of data and metadata including requirements for best practices, standards, curation and archiving, and interoperability requirements. Models for implementation and governance of the system were proposed, and report findings have been used in development of a pilot project to implement CIOOS.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".