Protective refuges for seeded juvenile scallops (<i>Placopecten magellanicus</i>) from sea star (<i>Asterias</i> spp.) and crab (<i>Cancer irroratus</i> and <i>Carcinus maenas</i>) predation
Bibliographic record
Abstract
We examined two methods to provide refuge for seeded juvenile sea scallops (Placopecten magellanicus) from sea star (Asterias spp.) and crab (Cancer irroratus and Carcinus maenas) predation by considering (i) initial density of seeded scallops and (ii) presence of an alternative prey species (blue mussel (Mytilus edulis)). In the seeding density experiment, underwater plots were seeded with different densities of scallops (1, 6, and 69·m2). In the alternative prey experiment, plots were seeded with one density of scallops (5·m2) and different densities of mussels (0, 5, and 30·m2). Animal densities were monitored over time, and predation rate was estimated using tethered scallops. In the seeding density experiment, scallop density in plots initially seeded with 6 scallops·m2 decreased at the slowest rate. Estimated predation rate of scallops in all plots tended to increase with prey density. In the alternative prey experiment, mussel density decreased immediately after seeding, while scallop density decreased after approximately 1 week. Estimated predation rate of scallops decreased with increasing mussel density. Also, sea stars aggregated in plots containing scallops and mussels. In both experiments, 17%58% of seeded scallops were lost to dispersal, and final scallop density was approximately 1·m2, independent of treatment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".