CIOOS: Scaling Up in this Ocean Decade
Bibliographic record
Abstract
This is a presentation delivered as part of Ocean Sciences Meeting 2022 (AGU/ASLO/TOS), in session OS11: Observing and Predicting the Global Coastal Ocean 03. This presentation was recorded and is available here: https://www.youtube.com/watch?v=GKi7ZlCfQYk The Canadian Integrated Ocean Observing System (CIOOS) launched in 2019, following a decade of initiatives seeking nationally coordinated ocean observations. CIOOS completed its pilot phase, where it built infrastructure, defined metadata standards, prioritized EOVs, and established relationships. As we embark on the UN Ocean Decade and Canada’s contribution to a transparent and predictable ocean, we identify five 10-year horizon key challenges. 1. Streams and scale: We anticipate a transition from mostly static data to streams in the cloud. This will strain our capacity and conceptual models. This means investment in data processing infrastructure, co-locating analysis with data, and building ocean-to-desktop systems. 2. Principles and practices: CIOOS seeks to meet the FAIR, CARE and TRUST Principles, and to integrate and develop best practices relevant to the oceanographic and data management communities. Key focus areas include biological data, marine debris and model outputs. 3. Indigenous data and knowledge: CIOOS is forming partnerships with First Nations in coastal areas. Through co-design, CIOOS can integrate digital frameworks that meet the CARE (Collective benefit, Authority to control, Responsibility and Ethics) Principles and recognize sensitive data concerns. The application of Traditional Knowledge and Biocultural labels is a plausible approach for customizable data usage constraints. 4. Private sector engagement: These companies are an important producer and user of data. The underway VITALITY project has a mission to facilitate sharing of private sector data and to deliver outcomes that will build the blue economy. 5. Global collaboration: CIOOS recognizes partnerships and interoperability with observatories in adjacent waters are essential for applications that transcend national borders. Plans include participation in international working groups, co-development with the US IOOS, and data inputs to the Global Telecommunications System.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".