Application of ISSR, RAPD, and cytological markers to the certification of <i>Picea mariana, P. glauca</i>, and <i>P. engelmannii</i> trees, and their putative hybrids
Bibliographic record
Abstract
Picea glauca (white spruce) and P. engelmannii (Engelmann spruce) are so similar and integrated that it is impossible to distinguish between them and their hybrids using morphological characteristics. Although natural hybrids between P. glauca and P. mariana (black spruce) do not generally occur, even though the 2 species are sympatric in North America, a first-generation hybrid, called the Rosendahl spruce, has been reported in the literature. In this study, several inter-simple sequence repeat (ISSR) markers were developed, as were randomly amplified polymorphic DNA (RAPD) markers, to certify spruce trees and their hybrids. ISSR fingerprinting was more efficient than RAPD assay; it detected 70% polymorphic DNA markers among the spruce species analyzed, whereas RAPD fingerprinting detected only 53%. Species-diagnostic ISSR and RAPD markers differentiating P. glauca from P. engelmannii and P. mariana were cloned and sequenced. Molecular certification of the spruce samples analyzed confirmed that all the seeds from interior spruce populations were true hybrids of P. glauca and P. engelmannii. But the analysis of seeds derived from the putative Rosendahl spruce indicated that this tree is likely a pure P. glauca genotype, rather than a hybrid of P. glauca and P. mariana. These data were confirmed by cytological analyses. Further analysis, using a more sensitive DNA amplification method with designed primers flanking the species-diagnostic ISSR and RAPD markers, revealed that such sequences are not generally species-specific because they are present in other spruce 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 | 0.001 | 0.001 |
| 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".