Dreissenid Mussel Research Priorities Workshop
Bibliographic record
Abstract
Currently, dreissenid mussels have yet to be detected in the northwestern part of the United States and western Canada. Infestation of one of the jurisdictions within the mussel-free Pacific Northwest would likely have significant economic, societal and environmental implications for the entire region. Understanding the biology and environmental tolerances of dreissenid mussels, and effectiveness of various management strategies, is key to prevention. On November 4-5, 2015, the Aquatic Bioinvasion Research and Policy Institute and the Center for Lakes and Reservoirs at Portland State University, the US Geological Survey, and the Pacific States Marine Fisheries Commission, convened a Dreissenid Mussel Research Priorities Workshop funded by the Great Northern Landscape Conservation Cooperative. The purpose of the workshop was to review dreissenid research priorities in the 2010 Quagga-Zebra Mussel Action Plan for Western U.S. Waters, reassess those priorities, incorporate new information and emerging trends, and develop priorities to strategically focus research efforts on zebra and quagga mussels in the Pacific Northwest and ensure that future research is focused on the highest priorities. It is important to note that there is some repetition among dreissenid research priority categories (e.g., prevention, detection, control, monitoring, and biology). Workshop participants with research experience in dreissenid mussel biology and management were identified by a literature review. State and federal agency managers were also invited to the workshop to ensure relevancy and practicality of the workshop outcomes. A total of 28 experts (see sidebar) in mussel biology, ecology, and management attended the workshop.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".