Narrowing the search for European green crab in Washington’s inland waters
Bibliographic record
Abstract
The invasive, fist-sized European green crab was first found in waters adjacent to the Salish Sea in 1998 after warm El Nino currents spread larvae of California populations up the Eastern Pacific coast to estuaries as far north as Vancouver Island. Most outer coast populations had limited establishment success, persisting as either small or geographically constrained populations. Because of the potential risk the invasive crab posed to coastal resources, it was designated a deleterious species in Washington State, placing strict constraints on possession and transport of the species and giving the Washington Department of Fish and Wildlife authority to monitor and control the crabs. The Salish Sea appeared to be green crab-free until Fisheries and Oceans Canada staff discovered a thriving population in 2012 in Sooke Inlet near Victoria, British Columbia. To help inform monitoring and public outreach efforts, students from the University of Washington worked with staff from Washington Sea Grant and the UW School of Aquatic and Fishery Sciences to identify, map and prioritize suitable green crab habitat using course physical, biological and access characteristics that could be observed in satellite imagery. Nearly 100 locations appear to have high likelihood of being suitable for European green crab establishment. In August 2013, the authors surveyed for crab molts at select high priority locations on the Strait of Juan de Fuca and around Hood Canal and found no green crab molts. The surveys will be repeated in 2014 with outreach to coastal communities around selected priority locations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".