Comparison of trapping methods for invasive European green crab
Bibliographic record
Abstract
European Green Crab (EGC) (Carcinus maenas) is a voracious aquatic invasive species (AIS) that poses a serious threat to Canada’s marine and estuarine ecosystems on the Atlantic and Pacific coasts. Fisheries and Oceans Canada (DFO), in partnership with stakeholders and Indigenous groups, has developed substantial knowledge of EGC and its trapping. Trapping has been used for early detection, monitoring, research, and physical removal for control. A review of peer reviewed studies and unpublished projects on EGC trapping was conducted to examine different trap types and their usage in Canada and in other locations where EGC have been trapped. Several factors are key in selecting an appropriate trap type based on trapping objectives. Applications of the different trap types, including important characteristics and deployment logistics are provided in a summary table. Trapping is an effective method for early detection and monitoring relative changes in EGC abundances, population dynamics, and native species. Trapping for rapid response and control can effectively reduce EGC numbers and alter population dynamics. Outcomes could include reduction of mean body size of EGC and recovery of impacted native species and habitat, but trapping efforts may need to be sustained. Knowledge gaps and challenges identified include a lack of information on capturing juvenile EGC in Canada and determining effective threshold levels or numbers for control to prevent environmental and fishery impact.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".