The Lobster Node of the CFRN: co-constructed and collaborative research on productivity, stock structure, and connectivity in the American lobster (<i>Homarus</i> <i>americanus</i>)
Bibliographic record
Abstract
In 2010, more than 20 associations representing harvesters from five provinces bordering the range of American lobster (Homarus americanus) in Canada, from the Gulf of Maine to southern Labrador, joined government research scientists at Fisheries and Oceans Canada (and one provincial department) and researchers from Canadian universities (two English- and four French-speaking) to establish the Lobster Node. This partnership was formed to address knowledge gaps on lobster productivity, stock structure, and connectivity through collaborative research under the auspices of the Canadian Fisheries Research Network (CFRN), which was funded by the Natural Sciences and Engineering Research Council of Canada. In so doing, the research partners overcame barriers of geography, language, culture, education, and, in some cases, longstanding disputes around management and conservation measures. This paper reviews why and how the Lobster Node was formed, what it achieved scientifically, what benefits (and challenges) it provided to the partners, and why it succeeded. It concludes by advocating for the creation of a permanent collaborative platform to conduct research in support of lobster fisheries in Canada.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".