MICROSATELLITE MARKER DEVELOPMENT AND GENETIC VARIATION IN THE TOXIC MARINE DIATOM <i>PSEUDO‐NITZSCHIA MULTISERIES</i> (BACILLARIOPHYCEAE)<sup>1</sup>
Bibliographic record
Abstract
The genetic structure of phytoplankton populations is largely unknown. In this study we developed nine polymorphic microsatellite markers for the domoic acid–producing marine diatom Pseudo‐nitzschia multiseries (Hasle) Hasle. We then used them in the genotyping of 25 physiologically diverse field isolates and six of their descendants: 22 field isolates originated from eastern Canadian waters, two from European waters, and one from Russian waters. The numbers of alleles per locus ranged from three to seven and the observed heterozygosities from 0.39 to 0.70. A substantial degree of genetic variation was observed within the field isolates, with 23 different genotypes detected. The Russian isolate was the most genetically distinct, although there was also evidence of genetic differentiation at a more local scale. Mating experiments demonstrated that alleles were inherited in a Mendelian manner. Pseudo‐nitzschia multiseries primer pairs were tested on DNA from four congeners: P. calliantha Lundholm, Moestrup et Hasle; P. fraudulenta (P. T. Cleve) Hasle; P. pungens (Grunow ex P. T. Cleve) Hasle; and P. seriata (P. T. Cleve) H. Peragallo. Cross‐reactivity was only observed in P. pungens. Our results are a first step in understanding the genetic variation present at the Pseudo‐nitzschia“species” level and in determining the true biogeographic extent of Pseudo‐nitzschia species.
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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) | 0.000 | 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".