Muscular Coloration Diversity and Microsatellite Genetic Differentiation for the Clam <i>Paphia amabilis</i> (Philippi, 1847)
Bibliographic record
Abstract
Paphia amabilis is an important fishery shellfish widely distributing along Southeast Asia coast. The muscles of its foot and siphons are commonly colored bright red-orange due to the presence of C37 skeletal carotenoids. However, 10.5%-18.6% of yellow-muscled individuals (YMs) were found among the four P . amabilis wild samples from Vietnam (Co To island, CT) and China (Beihai, BH/BHA, Guangxi Province; and Zhanjiang, ZJ, Guangdong Province), the north of Beibu Gulf, South China Sea. Twenty genomic microsatellite markers were developed and 12 were employed to analyze the population genetic differentiation of three geographical populations (CT, BH, and ZJ, n=32/population), as well as YM and RM (red-muscled) subpopulations. Medium levels of genetic diversity were detected in all three populations with the number of alleles ranging from 4-21 (8.4±4.5) for CT, 4-16 (7.6±3.3) for BH and 4-18 (7.5±3.7) for ZJ. The average observed/expected heterozygosities were 0.66/0.66 for CT, 0.62/0.63 for BH, and 0.55/0.62 for ZJ, respectively. Total 4 out of 36 tests of microsatellites deviated from Hardy–Weinberg equilibrium (HWE, P <0.05 after Bonferroni correction), all in ZJ population due to the presence of null alleles suggested by MICRO-CHECK. Exact test indicated highly significant differentiations ( F ST ) between Vietnamese and two Chinese populations (0.099/0.123 between CT and BH/ZJ). No allele of any microsatellite marker was found significantly associated with muscularcoloration of P. amabilis , consequently no convergent microsatellite genetic differentiation presents between YM and RM subpopulations, supporting that epigenetic factor might play an important role in carotenoids accumulation of P. amabilis .
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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 | 0.000 | 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) | 0.002 | 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".