Developing a collaborative Dungeness crab larval monitoring network in the Salish Sea to provide long-term, fishery-relevant data
Bibliographic record
Abstract
Abstract Many natural resources are managed without essential, biologically relevant data. Fisheries are particularly susceptible to this reality and, thus, are vulnerable to environmental changes and disturbances, with both human livelihoods and the health of ecological systems at stake. Here, we explore how the Pacific Northwest Crab Research Group (PCRG) employs a collaborative, stakeholder-driven approach to generate the information needed to inform a data-poor, co-managed fishery, using the example of Dungeness crab Metacarcinus magister in the northeastern Pacific’s Salish Sea. We focus on the PCRG larval crab monitoring network as a multifaceted case study, which unites tribal, state, and federal governments, nongovernmental organizations, academic institutions, and local communities working to produce the first standardized continuous data set on Dungeness crab larval dynamics in the region. Highlighting the types of biological data collected, including spatial and temporal patterns of larval flux and larval size, we explore the application of novel data to fisheries management, as well as the network’s contribution to a diversity of educational opportunities and ability to leverage new research projects and collaborations. The success of PCRG’s larval crab monitoring network ultimately highlights the effectiveness of a cooperative, network-based approach in addressing fisheries management challenges and offers a viable model for managing data-poor systems worldwide.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".