Collaborative fisheries research: the Canadian Fisheries Research Network experience
Bibliographic record
Abstract
The Canadian Fisheries Research Network (CFRN) was a collaboration among fish harvesters, academic researchers, and government scientists that undertook research between 2010 and 2016 on questions about fisheries that were identified by fish harvesters and pertinent to management objectives. This paper provides a synthesis of the scope and results of the CFRN. It explores the link between the increasing challenges to fisheries sustainability and the need for increased research capacity and for a collaborative approach. It documents the creation of the collaboration, the research it accomplished, and its benefits and explores the need for ongoing collaboration. The papers in this special issue on the CFRN demonstrate the benefits of collaborative fisheries research that are of relevance internationally and support the need for a permanent collaborative platform to conduct research to support fisheries management capacity and decision-making in Canada.
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 | 0.058 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".