Genetic differentiation in relation to marine landscape in a broadcast‐spawning bivalve mollusc (<i>Placopecten magellanicus</i>)
Bibliographic record
Abstract
Marine bivalves are sessile or sedentary as adults but have planktonic larvae which can potentially disperse over large distances. Consequently larval transport is expected to play a prominent role in facilitating gene flow and determining population structure. The sea scallop (Placopecten magellanicus) is a dioecious species with high fecundity, broadcast spawning and a c. 30-day planktonic larval stage, yet it forms discrete populations or 'beds' which have significantly different dynamics and characteristics. We analysed variation at six microsatellite loci in 12 locations throughout the geographic range of the species from Newfoundland, Canada, to New Jersey, USA. Significant differentiation was present and the maximum pairwise theta value, between one of the Newfoundland samples in the north and a sample from the southern portion of the range, was high at 0.061. Other proximate pairs of samples had no detectable genetic differentiation. Mantel tests indicated a significant isolation by distance, but only when one of the populations was excluded. A landscape genetic approach was used to detect areas of low gene flow using a joint analysis of spatial and genetic information. The two major putative barriers inferred by Monmonier's algorithm were then used to define regions for an analysis of molecular variance (amova). That analysis showed a significant but low percentage (1.2%) of the variation to be partitioned among regions, negligible variation among populations within regions, and the majority of the variance distributed between individuals within populations. Prominent currents were concordant with the demarcation of the regions, while a novel approach of using particle tracking software to mimic scallop larval dispersal was employed to interpret within-region genetic patterns.
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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 | 0.000 |
| 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) | 0.000 | 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".