A Network Approach to Addressing Strategic Fisheries, Aquaculture, and Aquatic Sciences Issues at a National Scale: An Introduction to a Series of Case Studies from Canada
Bibliographic record
Abstract
Abstract Traditional funding programs for fisheries, aquaculture, and aquatic research provide short-term support for an individual or small research team to test a specific hypothesis, often having only limited spatial applicability. To tackle more complex issues existing at larger spatial scales (national or continental), other approaches are necessary. In Canada, the Natural Sciences and Engineering Research Council has developed the Strategic Network Grants (SNGs) program that enables multi-institutional teams of academics (typically 10 to 20 co-principal investigators) to work with industry and government partners on large-scale, multidisciplinary research projects in targeted research areas. The network model is intended to create unique training opportunities and enable researchers to study problems at spatial and temporal scales that could not be addressed with traditional funding. Currently, six of the 30-plus SNGs in Canada are focused on fisheries, aquaculture, and aquatic sciences issues, namely, impacts of hydropower on fish and fish habitat, capture fisheries, integrated multitrophic aquaculture, healthy oceans, and the spatial ecology of aquatic vertebrates in coastal waters. Here we introduce five case studies that will examine the motivation, scientific research objectives, and operation of networks in detail. In addition, we explore the perceived benefits and challenges with the research network-funding model with specific reference to the advancement of large-scale studies in fisheries, aquaculture, and aquatic sciences.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".