Identification and Characterization of Microsatellite Markers Useful for Genetic Analysis of Black Spruce (Picea mariana (Mill.) Populations
Bibliographic record
Abstract
Summary Large - scale isolation of microsatellite and information in any conifer species is limited. Our knowledge of microsatellite in spruce (Picea spp.) is still sketchy. Genomic libraries of P. mariana were constructed and screened with (AC)15 probes. Over 200 positive clones were found for this dinucleotide and ten were analyzed in details. They were sequenced to confirm the presence of microsatellites and to generate polymerase chain reaction (PCR) primers based on sequences flanking the microsatellites. All sequenced (AC)n clones had repeats of n > 23. Primer pairs were designed from seven sequences. These primer pairs along with 15 primer pairs from white spruce (Picea glauca) were tested on individual trees. Seven primer pairs from P. mariana and three from P. glauca (white spruce) amplified DNA from P. mariana and were used for genetic analysis of black spruce populations from uplands (drylands) and lowlands (wetlands). High levels of polymorphism and heterozygosity were observed in all the populations studied. Both highlands and lowlands showed similar levels of genetic variation. The selected microsatellites sequences are being used for genome organization analysis of black spruce.
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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 | 0.001 |
| 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) | 0.001 | 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".