The Marine Environmental Observation, Prediction and Response Network (MEOPAR): An Interdisciplinary, Networked Approach to Building Canada’s Marine Research Capacity
Bibliographic record
Abstract
The Marine Environmental Observation, Prediction and Response Network (MEOPAR) is an interdisciplinary Canadian Network of Centres of Excellence, connecting leading marine researchers across the country with trainees, partners and communities. MEOPAR funds research, trains Highly-Qualified Personnel, develops strategic partnerships, and works to support knowledge mobilization in marine challenges and opportunities for the benefit of the Canadian economy and society. As a Network, MEOPAR’s strength lies in our inter-sectoral connections—to researchers, partners, organizations, and Indigenous communities, all of whom have an interest in learning more about risks and opportunities in the marine environment. The Network funds research focusing on the North Atlantic, St. Lawrence, Arctic Ocean, and Salish Sea. MEOPAR has trained over 700 Highly-Qualified Personnel (“MEOPeers”) since 2012. One in three MEOPeers are international students or researchers who have chosen to study or progress in their research careers in Canada. MEOPAR’s training program builds capacity in interdisciplinary research and 21st-century skills related to marine environmental risk and the required response and policy strategies. Training content is based on MEOPAR's four outcome areas (Ocean Observation; Forecasting and Prediction; Coastal Resilience; and Marine Operations), along with core content areas relevant to Canada’s next generation of marine professionals (Knowledge Translation and Science Communication; Interdisciplinary Research; and Career Development). To help build capacity in marine research, MEOPAR offers a suite of training initiatives to post-secondary students and early-career researchers, including a Postdoctoral Fellowship Award, Early Career Faculty grants, travel awards, workshops, International Research Internship and Visiting Scholar funding, and an Annual Training Meeting. These initiatives provide MEOPeers with value-added training opportunities they would not be able to access through their academic programs or research labs. This poster will introduce MEOPAR’s interdisciplinary and intercultural approaches to training the next generation of marine leaders in Canada. Case studies will feature MEOPeers working in the North Atlantic region who are pursuing value-added training opportunities supported by the Network.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".