Predation of Sea Scallops and Other Indigenous Bivalves by Invasive Green Crab,<i>Carcinus maenas</i>, from Newfoundland, Canada
Bibliographic record
Abstract
Populations of green crab (Carcinus maenas) have expanded within Newfoundland, and this has raised concern from fish harvesters and scientists regarding bivalve predation in coastal areas on species such as juvenile sea scallops (Placopecten magellanicus). We used 2 microcosm experiments to determine (1) the effects of water temperature (5°C and 12°C) on scallop predation; (2) scallop size selection in small and large green crabs and a large indigenous predator, the rock crab (Cancer irroratus); and (3) bivalve prey selection in large green crabs between softshell clams (Mya arenaria), blue mussels (Mytilus edulis), and sea scallops. Overall, green and rock crabs captured 4 times more scallops in warm water (12°C) than cold (5°C). Large green (60–70 mm) and rock (75–90 mm) crabs captured similar numbers of scallops, selected medium-size (30–40 mm) scallops, and avoided small (10–20 mm) scallops. Small green crabs (40–50 mm) captured only small scallops. Large green crabs selected softshell clams and blue mussels over scallops. Overall, our research demonstrated that both green and rock crabs can prey on similar sizes of scallops, and suggests that green crabs may be a new predation threat to the shallow coastal scallop populations in Newfoundland.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".