Revisiting recent history: records of occurrence and expansion of the European green crab across Prince Edward Island, Atlantic Canada
Bibliographic record
Abstract
Late in the 1990s, the non-indigenous European green crab ( Carcinus maenas ) colonized the shorelines of eastern Prince Edward Island, in Atlantic Canada. Due to concerns of further spread into productive shellfish habitats, an annual survey was conducted between 2000 and 2013 to detect a potential range expansion of this species. We compiled and analyzed that data and document green crab expansion using records of annual occurrence and relative density. Surveys were conducted during the fall season of each year by deploying baited traps at 29 sites along the island’s two main shorelines (north and south shores). These sites were selected based on areas deemed more likely to be invaded by the green crab. Raw data per site and date was transformed to catch per unit effort (CPUE) to estimate relative abundances. Populations of this species showed an uneven westward expansion along the north and south shores. Expansion rates changed among years but, overall, crab abundance was higher and changes in abundance were faster along the south shore than the north shore of the island. The westward expansion continues until this day. Based on the information compiled we hypothesize that the dissimilarity in range expansion rate was related to the availability of suitable habitat to sustain large green crab populations along the south shore. We also discuss implications of this expansion for commercial shellfish and native coastal communities.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".